Trabajo Integrador Covid-19

Alumna: Mora Assereto Farroni

Objetivo: Construir un modelo lineal que nos permita comprender cuales fueron los factores determinantes en las muertes que se produjeron por el Covid-19, que tuvo una amplia variabilidad en diferentes países.

Análisis exploratorio de datos

El modelo se basa en una serie da variables demográficas, geográficas y de salud publica obtenidad de 139 países:

  • Hombres80 población de hombres mayor a 80 años (% de la población masculina).

  • Mujeres80 población de mujeres mayor a 80 (% de la población femenina).

  • Pobla80: promedio entre Female80 y Male80.

  • Pobla65: población mayor a 65 años (% de la población).

  • PoblaMid: población entre 15 y 64 años (% de la población).

  • PoblaData: población en 2018 (en 100 millones de personas).

  • PoblaDens: densidad poblacional (cientos de personas por km cuadrado de superficie)

  • Mujeres: Población femenina (% de la población total)

  • Urbano: Población urbana (% de la población total)

  • ExpectVida: Esperanza de vida al nacer, total (años)

  • NeontlMort: Tasa de mortalidad neonatal, neonatal (por 1000 nacidos vivos)

  • DisMort: Mortalidad por enfermedades cardiovasculares, cáncer, diabetes o enfermedad renal crónica entre las edades exactas de 30 y 70 (%)

  • Lesion: Causa de muerte por lesión (% del total)

  • EnfNoTrans: Causa de muerte por enfermedades no transmisibles (% del total)

  • EnfTrans: Causa de muerte por por enfermedades transmisibles y materna, prenatal y condiciones nutricionales (% del total)

  • PBI: producto bruto interno per cápita PPP (miles de dolares internacionales corrientes)

  • Tuberculosis: Incidencia de tuberculosis (por 1000 personas)

  • Diabetes: Prevalencia de la diabetes ( % de la población de 20 a 79 años)

  • Medicos: Médicos (cada 1000 personas)

  • Camas: Camas de hospital (cada 1000 personas)

  • ImmunSaramp: Inmunización de sarampión (% de chicos entre 12 y 23 meses)

  • TempMarzo: Temperatura promedio en marzo.

  • HipTen.H: Prevalencia bruta de hipertensión en 2010 en hombres

  • HipTen.M: Prevalencia bruta de hipertensión en 2010 en mujeres

  • HipTen: Promedio de HT.women y HT.men

  • BCG: Estrategia de inmunización 0 = selectiva, 1 = todos.

  • BCGf: Es la variables BCG escrita como un factor.

  • Tiempo: número de días entre el primer caso de COVID-19 registrado y el 31 de diciembre de 2019.

  • geoid: ID para identificar la zona

  • CntrName : País

Variable de respuesta:

l10muertes.permil: log10(muertes.permil+1) donde muertes.permil es el número de muertos cada millón de habitantes.

Librerias

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(GGally)
## Loading required package: ggplot2
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
library(ggplot2)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ tibble  3.1.7     ✔ purrr   0.3.4
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(hrbrthemes)
## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
##       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
##       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(ggpubr)
library(cowplot)
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggpubr':
## 
##     get_legend
library(caTools)
library(corrplot)
## corrplot 0.92 loaded
library(Hmisc)
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## 
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:dplyr':
## 
##     src, summarize
## The following objects are masked from 'package:base':
## 
##     format.pval, units
library(multcomp)
## Loading required package: mvtnorm
## Loading required package: TH.data
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
## 
## Attaching package: 'TH.data'
## The following object is masked from 'package:MASS':
## 
##     geyser
library(readxl)
library(gmodels)
library(ggthemes)
## 
## Attaching package: 'ggthemes'
## The following object is masked from 'package:cowplot':
## 
##     theme_map
library(devtools)
## Loading required package: usethis
library(car)
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:purrr':
## 
##     some
## The following object is masked from 'package:dplyr':
## 
##     recode
library(leaflet)
library(psych)
## 
## Attaching package: 'psych'
## The following object is masked from 'package:car':
## 
##     logit
## The following object is masked from 'package:Hmisc':
## 
##     describe
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

Cargamos el dataset

covid <- read.table('COVID.txt', header = TRUE)
covid
##    geoId                         CntrName muertes   casos muertes.permil
## AF    AF                      Afghanistan     227   12456     6.10668360
## AL    AL                          Albania      33    1050    11.51279525
## DZ    DZ                          Algeria     623    8857    14.75309441
## AO    AO                           Angola       4      71     0.12982898
## AR    AR                        Argentina     500   13920    11.23734344
## AM    AM                          Armenia      98    7774    33.20035125
## AU    AU                        Australia     103    7139     4.12125797
## AT    AT                          Austria     645   16515    72.90576495
## AZ    AZ                       Azerbaijan      54    4568     5.43132025
## BD    BD                       Bangladesh     544   38292     3.37142634
## BY    BY                          Belarus     214   38956    22.56102177
## BE    BE                          Belgium    9364   57592   819.81651659
## BZ    BZ                           Belize       2      18     5.22096426
## BJ    BJ                            Benin       3     210     0.26120918
## BT    BT                           Bhutan       0      28     0.00000000
## BO    BO                          Bolivia     280    7768    24.66277617
## BA    BA           Bosnia_and_Herzegovina     151    2435    45.42816649
## BW    BW                         Botswana       1      35     0.44363092
## BR    BR                           Brazil   25598  411821   122.20404597
## BN    BN                Brunei_Darussalam       2     141     4.66241765
## BG    BG                         Bulgaria     133    2460    18.93449746
## BF    BF                     Burkina_Faso      53     845     2.68333575
## BI    BI                          Burundi       1      42     0.08948243
## KH    KH                         Cambodia       0     124     0.00000000
## CM    CM                         Cameroon     177    5436     7.01928682
## CA    CA                           Canada    6765   87508   182.54745910
## CF    CF         Central_African_Republic       1     702     0.21429902
## CO    CO                         Colombia     803   24104    16.17364085
## KM    KM                          Comoros       2      87     2.40291618
## CG    CG                            Congo      19     571     3.62293762
## HR    HR                          Croatia     101    2244    24.69799971
## CZ    CZ                   Czech_Republic     317    9086    29.83334267
## DK    DK                          Denmark     565   11480    97.45670766
## DJ    DJ                         Djibouti      18    2697    18.77111751
## DO    DO               Dominican_Republic     474   15723    44.60267625
## EC    EC                          Ecuador    3275   38103   191.69583028
## EG    EG                            Egypt     816   19666     8.29069493
## SV    SV                      El_Salvador      39    2109     6.07406245
## GQ    GQ                Equatorial_Guinea      12    1043     9.16748537
## EE    EE                          Estonia      66    1840    49.96653756
## ET    ET                         Ethiopia       6     731     0.05493270
## FJ    FJ                             Fiji       0      18     0.00000000
## FI    FI                          Finland     313    6692    56.72293654
## FR    FR                           France   28596  145746   426.88724438
## GA    GA                            Gabon      14    2319     6.60603272
## GM    GM                           Gambia       1      25     0.43857687
## GE    GE                          Georgia      12     735     3.21629590
## DE    DE                          Germany    8411  179717   101.42542822
## GH    GH                            Ghana      34    7303     1.14220031
## EL    EL                           Greece     173    2903    16.12652442
## GT    GT                        Guatemala      68    4145     3.94253020
## GN    GN                           Guinea      21    3446     1.69159514
## GY    GY                           Guyana      11     139    14.12059502
## HT    HT                            Haiti      34    1320     3.05668093
## HN    HN                         Honduras     194    4640    20.23463414
## HU    HU                          Hungary     509    3816    52.10473974
## TD    TD                             Chad      64     715     4.13496767
## CL    CL                            Chile     841   82289    44.90324179
## CN    CN                            China    4638   84106     3.33015014
## IN    IN                            India    4531  158333     3.34980183
## ID    ID                        Indonesia    1473   23851     5.50317977
## IR    IR                             Iran    7564  141591    92.46913357
## IQ    IQ                             Iraq     175    5135     4.55330752
## IE    IE                          Ireland    1631   24803   336.04573683
## IL    IL                           Israel     281   16793    31.63060852
## IT    IT                            Italy   33072  231139   547.26622303
## JM    JM                          Jamaica       9     569     3.06659102
## JP    JP                            Japan     858   16651     6.78104879
## JO    JO                           Jordan       9     720     0.90397650
## KZ    KZ                       Kazakhstan      37    9576     2.02445775
## KE    KE                            Kenya      55    1471     1.07018445
## KW    KW                           Kuwait     175   23267    42.29802512
## LV    LV                           Latvia      23    1057    11.93848875
## LB    LB                          Lebanon      26    1161     3.79621619
## LS    LS                          Lesotho       0       2     0.00000000
## LR    LR                          Liberia      27     266     5.60284890
## LY    LY                            Libya       4      99     0.59893088
## LT    LT                        Lithuania      66    1647    23.65987425
## LU    LU                       Luxembourg     110    4001   181.00202722
## MG    MG                       Madagascar       2     612     0.07615460
## MW    MW                           Malawi       4     101     0.22046688
## MY    MY                         Malaysia     115    7619     3.64748370
## ML    ML                             Mali      70    1116     3.66920733
## MR    MR                       Mauritania      16     292     3.63362273
## MU    MU                        Mauritius      10     334     7.90324531
## MX    MX                           Mexico    8597   78023    68.12700147
## MD    MD                          Moldova     274    7537    77.27271317
## MN    MN                         Mongolia       0     161     0.00000000
## ME    ME                       Montenegro       9     324    14.46143216
## MA    MA                          Morocco     202    7601     5.60657321
## MZ    MZ                       Mozambique       1     227     0.03390295
## MM    MM                          Myanmar       6     206     0.11171438
## NM    NM                          Namibia       0      22     0.00000000
## NP    NP                            Nepal       4     886     0.14241022
## NL    NL                      Netherlands    5871   45768   340.72277916
## NZ    NZ                      New_Zealand      22    1154     4.50312148
## NI    NI                        Nicaragua      35     759     5.41333688
## NE    NE                            Niger      64     955     2.85167528
## NO    NO                           Norway     235    8383    44.22001168
## OM    OM                             Oman      38    8373     7.86833705
## PK    PK                         Pakistan    1260   61227     5.93737399
## PA    PA                           Panama     315   11728    75.41526879
## PY    PY                         Paraguay      11     884     1.58135246
## PE    PE                             Peru    3983  135905   124.51055442
## PH    PH                      Philippines     904   15049     8.47617167
## PL    PL                           Poland    1028   22473    27.06791213
## PT    PT                         Portugal    1356   31292   131.88400976
## QA    QA                            Qatar      30   48947    10.78486107
## RO    RO                          Romania    1219   18594    62.59648794
## RW    RW                           Rwanda       0     346     0.00000000
## VC    VC Saint_Vincent_and_the_Grenadines       0      18     0.00000000
## SA    SA                     Saudi_Arabia     425   78541    12.61129580
## SN    SN                          Senegal      39    3253     2.45989116
## RS    RS                           Serbia     240   11275    34.37369129
## SL    SL                     Sierra_Leone      45     782     5.88223453
## SG    SG                        Singapore      23   32876     4.07897173
## SK    SK                         Slovakia      28    1515     5.14043390
## SI    SI                         Slovenia     107    1471    51.75652955
## ZA    ZA                     South_Africa     552   25937     9.55354121
## KR    KR                      South_Korea     269   11344     5.20961879
## ES    ES                            Spain   27118  236769   580.39007101
## LK    LK                        Sri_Lanka      10    1469     0.46146747
## SE    SE                           Sweden    4220   35088   414.40906201
## CH    CH                      Switzerland    1647   30678   193.38832670
## TJ    TJ                       Tajikistan      46    3100     5.05448015
## TH    TH                         Thailand      57    3054     0.82098822
## TG    TG                             Togo      13     395     1.64784448
## TN    TN                          Tunisia      48    1051     4.15038075
## TR    TR                           Turkey    4397  158762    53.41368734
## UG    UG                           Uganda       0     281     0.00000000
## UA    UA                          Ukraine     644   21584    14.43217590
## AE    AE             United_Arab_Emirates     255   31969    26.47711407
## UK    UK                   United_Kingdom   37460  267240   563.40154117
## US    US         United_States_of_America  100442 1699933   307.00488362
## UY    UY                          Uruguay      22     803     6.37810755
## UZ    UZ                       Uzbekistan      14    3333     0.42481657
## YE    YE                            Yemen      53     255     1.85973480
## ZM    ZM                           Zambia       7    1057     0.40341585
## ZW    ZW                         Zimbabwe       4     132     0.27702715
##    casos.permil l10muertes.permil  Hombres80  Mujeres80   Pobla80   Pobla65
## AF 3.350874e+02        0.85166698 0.23332582  0.3209823 0.2771541  2.584927
## AL 3.663162e+02        1.09735434 2.44807869  3.0339539 2.7410163 13.744736
## DZ 2.097402e+02        1.19736588 1.18175550  1.3602137 1.2709846  6.362497
## AO 2.304464e+00        0.05301271 0.20753263  0.3372488 0.2723907  2.216374
## AR 3.128476e+02        1.08768715 1.77481880  3.4475320 2.6111754 11.117789
## AM 2.633669e+03        1.53403057 2.45959960  3.7882699 3.1239347 11.253818
## AU 2.856472e+02        0.70937665 3.40801882  4.6792491 4.0436339 15.656475
## AT 1.866727e+03        1.86867832 3.92008681  6.5538665 5.2369767 19.001566
## AZ 4.594495e+02        0.80830014 0.98233980  1.7371450 1.3597424  6.195183
## BD 2.373137e+02        0.64062316 0.92567665  1.0671409 0.9964088  5.158391
## BY 4.106949e+03        1.37219412 1.98505780  5.6239517 3.8045048 14.845148
## BE 5.042169e+03        2.91424609 4.26847412  7.1212019 5.6948380 18.788744
## BZ 4.698868e+01        0.79385771 1.04770490  1.0313461 1.0395255  4.736459
## BJ 1.828464e+01        0.10078712 0.33308679  0.5400435 0.4365651  3.253605
## BT 3.711588e+01        0.00000000 1.20832395  1.2107077 1.2095158  6.003012
## BO 6.842159e+02        1.40930364 1.36894924  1.8807749 1.6248620  7.191947
## BA 7.325668e+02        1.66678153 2.62930836  4.1430706 3.3861895 16.470317
## BW 1.552708e+01        0.15945618 0.32968114  0.6582073 0.4939442  4.223874
## BR 1.966020e+03        2.09062497 1.38043099  2.2316251 1.8060280  8.922838
## BN 3.287004e+02        0.75300190 0.68594552  0.8308421 0.7583938  4.873148
## BG 3.502170e+02        1.29960529 3.47833930  5.9662322 4.7222857 21.021914
## BF 4.278149e+01        0.56624131 0.17806863  0.3125849 0.2453268  2.406981
## BI 3.758262e+00        0.03722023 0.22728873  0.3563006 0.2917947  2.246940
## KH 7.630864e+00        0.00000000 0.44915480  0.6916225 0.5703887  4.568680
## CM 2.155754e+02        0.90413575 0.24541433  0.3712980 0.3083562  2.728877
## CA 2.361325e+03        2.26374838 3.44384968  5.1704175 4.3071336 17.232007
## CF 1.504379e+02        0.08432564 0.22803331  0.4776179 0.3528256  2.825774
## CO 4.854912e+02        1.23486238 1.48426928  2.0351814 1.7597254  8.478047
## KM 1.045269e+02        0.53185125 0.31250447  0.4654681 0.3889863  3.007009
## CG 1.088788e+02        0.66491803 0.20761885  0.3678366 0.2877278  2.681720
## HR 5.487358e+02        1.40989932 3.64181309  7.4858273 5.5638202 20.445433
## CZ 8.550970e+02        1.48902061 2.83741960  5.3461832 4.0918014 19.420877
## DK 1.980182e+03        1.99324531 3.56907492  5.4657626 4.5174188 19.812953
## DJ 2.812539e+03        1.29603122 0.51375834  0.7060595 0.6099089  4.527579
## DO 1.479510e+03        1.65899033 1.41201959  1.7626442 1.5873319  7.082817
## EC 2.230286e+03        2.28487232 1.31184512  1.7634434 1.5376442  7.157290
## EG 1.998098e+02        0.96804820 0.59376508  0.9883030 0.7910340  5.229779
## SV 3.284666e+02        0.84966889 1.64624432  2.0376142 1.8419292  8.287090
## GQ 7.968073e+02        1.00721356 0.23244491  0.3448975 0.2886712  2.457877
## EE 1.393007e+03        1.70728513 3.14651675  7.9517097 5.5491132 19.626357
## ET 6.692634e+00        0.02322475 0.45453976  0.5479606 0.5012502  3.501133
## FJ 2.037391e+01        0.00000000 0.47150751  0.6876126 0.5795601  5.449680
## FI 1.212747e+03        1.76134842 3.92274373  6.8888011 5.4057724 21.720788
## FR 2.175728e+03        2.63132934 4.56728528  7.6391664 6.1032258 20.034625
## GA 1.094242e+03        0.88115819 0.38531522  0.6817970 0.5335561  3.563907
## GM 1.096442e+01        0.15793307 0.24379309  0.2753184 0.2595557  2.589981
## GE 1.969981e+02        0.62493108 2.51953653  4.8459528 3.6827447 14.865491
## DE 2.167147e+03        2.01040779 5.07035282  8.1838074 6.6270801 21.461962
## GH 2.453379e+02        0.33086008 0.27996116  0.3732648 0.3266130  3.068898
## EL 2.706087e+02        1.23366924 6.13905854  8.3967659 7.2679122 21.655272
## GT 2.403204e+02        0.69394933 0.93517499  1.1745195 1.0548473  4.812073
## GN 2.775827e+02        0.43000974 0.25836523  0.3807578 0.3195615  2.926022
## GY 1.784330e+02        1.17956888 1.07800787  1.4664646 1.2722362  6.450271
## HT 1.186711e+02        0.60817085 0.68159397  0.9736716 0.8276328  4.949404
## HN 4.839624e+02        1.32704478 0.85537137  1.2034197 1.0293956  4.690618
## HU 3.906320e+02        1.72513328 2.66613364  5.8764855 4.2713096 19.157725
## TD 4.619534e+01        0.71053771 0.26534733  0.3608357 0.3130915  2.480519
## CL 4.393630e+03        1.66184336 1.98522693  3.4164040 2.7008155 11.529802
## CN 6.038931e+01        0.63650295 1.39566195  2.1799124 1.7877871 10.920884
## IN 1.170568e+02        0.63846947 0.81547194  1.0741411 0.9448065  6.179956
## ID 8.910817e+01        0.81312576 0.63862200  1.0737409 0.8561815  5.857166
## IR 1.730936e+03        1.97066822 1.27963767  0.9669791 1.1233084  6.184574
## IQ 1.336071e+02        0.74455172 0.37591930  0.5902882 0.4831038  3.323600
## IE 5.110326e+03        2.52768884 2.50008058  3.5376243 3.0188525 13.865802
## IL 1.890295e+03        1.51362517 2.41256950  3.6798039 3.0461867 11.976986
## IT 3.824824e+03        2.73899149 5.48160915  8.8652623 7.1734357 22.751680
## JM 1.938767e+02        0.60923050 1.93886184  2.1524976 2.0456797  8.796643
## JP 1.315982e+02        0.89103814 6.16625352 10.5564415 8.3613475 27.576370
## JO 7.231812e+01        0.27966158 0.53354023  0.6712123 0.6023762  3.846490
## KZ 5.239516e+02        0.48064752 0.91633260  2.0700761 1.4932044  7.391846
## KE 2.862257e+01        0.31600904 0.19255206  0.3472758 0.2699139  2.339187
## KW 5.623704e+03        1.63646809 0.20842979  0.2570540 0.2327419  2.550472
## LV 5.486514e+02        1.11188355 3.14242031  7.9881716 5.5652960 20.043620
## LB 1.695157e+02        0.68089875 1.19514163  1.7705697 1.4828557  7.002368
## LS 9.487072e-01        0.00000000 0.37851920  1.2632823 0.8209008  4.901087
## LR 5.519844e+01        0.81973136 0.33809045  0.4611975 0.3996440  3.253432
## LY 1.482354e+01        0.20382969 0.62399259  0.8970569 0.7605248  4.392040
## LT 5.904214e+02        1.39199086 3.50274874  8.1713074 5.8370281 19.705033
## LU 6.583537e+03        2.26007623 3.00125285  5.0458011 4.0235270 14.183154
## MG 2.330331e+01        0.03187466 0.38401050  0.4643295 0.4241700  2.986717
## MW 5.566789e+00        0.08652600 0.20543741  0.4120391 0.3087382  2.645435
## MY 2.416537e+02        0.66721788 1.01429906  1.1340407 1.0741699  6.671755
## ML 5.849765e+01        0.66924316 0.24060395  0.2961390 0.2683715  2.507230
## MR 6.631361e+01        0.66592067 0.32220470  0.5186323 0.4204185  3.141112
## MU 2.639684e+02        0.94954834 1.45222816  2.5933641 2.0227961 11.474173
## MX 6.182939e+02        1.83964772 1.32388392  1.7807238 1.5523039  7.223685
## MD 2.125564e+03        1.89361039 1.43488921  3.0996156 2.2672524 11.469556
## MN 5.078531e+01        0.00000000 0.44312835  0.8136545 0.6283914  4.083539
## ME 5.206116e+02        1.18924972 2.64987500  4.1910961 3.4204855 14.974937
## MA 2.109681e+02        0.81997625 0.89534738  1.4420998 1.1687236  7.012905
## MZ 7.695969e+00        0.01447977 0.22502107  0.4712336 0.3481273  2.890764
## MM 3.835527e+00        0.04599322 0.57207835  0.9861366 0.7791075  5.784642
## NM 8.985992e+00        0.00000000 0.40544266  0.8249161 0.6151794  3.636032
## NP 3.154386e+01        0.05782208 0.73815416  0.7650848 0.7516195  5.727671
## NL 2.656140e+03        2.53367393 3.64646480  5.7488137 4.6976392 19.196193
## NZ 2.362092e+02        0.74060910 3.17823477  4.2839926 3.7311137 15.652425
## NI 1.173921e+02        0.80708405 0.87536492  1.3297727 1.1025688  5.247497
## NE 4.255234e+01        0.58564967 0.20098806  0.2671884 0.2340882  2.595008
## NO 1.577431e+03        1.65533067 3.20399502  5.2423112 4.2231531 17.049222
## OM 1.733726e+03        0.94784219 0.29164290  0.6276245 0.4596337  2.392787
## PK 2.885140e+02        0.84119511 0.66816849  0.6409555 0.6545620  4.312774
## PA 2.807842e+03        1.88318015 1.69079701  2.1502704 1.9205337  8.104731
## PY 1.270832e+02        0.41184731 1.01873059  1.4083568 1.2135437  6.430215
## PE 4.248458e+03        2.09868025 1.46155999  1.9002429 1.6809015  8.088393
## PH 1.411039e+02        0.97663292 0.52949911  1.0492596 0.7893793  5.122569
## PL 5.917288e+02        1.44821011 2.90312619  6.0231067 4.4631164 17.517817
## PT 3.043447e+03        2.12347272 4.92825496  7.7534849 6.3408699 21.953858
## QA 1.759622e+04        1.07132447 0.08736843  0.2820742 0.1847213  1.370070
## RO 9.548147e+02        1.80343313 3.42296861  5.9939190 4.7084438 18.338701
## RW 2.812565e+01        0.00000000 0.28365324  0.4412880 0.3624706  2.938196
## VC 1.633246e+02        0.00000000 2.20804973  2.6884001 2.4482249  9.589787
## SA 2.330597e+03        1.13389947 0.39373448  0.6139561 0.5038453  3.314088
## SN 2.051802e+02        0.53906244 0.31785259  0.4472135 0.3825330  3.086824
## RS 1.614847e+03        1.54868038 3.14271267  4.8955145 4.0191136 18.345793
## SL 1.022202e+02        0.83772947 0.27619394  0.4282340 0.3522140  2.966556
## SG 5.830447e+03        0.70577580 1.57583078  2.7774882 2.1766595 11.463380
## SK 2.781342e+02        0.78819906 2.06381766  4.3809082 3.2223629 15.629247
## SI 7.115314e+02        1.72227622 3.49460207  7.1359265 5.3152643 19.606880
## ZA 4.488953e+02        1.02339821 0.46387285  0.9730718 0.7184723  5.318005
## KR 2.196949e+02        0.79306494 2.07437980  4.3497052 3.2120425 14.418556
## ES 5.067423e+03        2.76446761 4.67746877  7.6569426 6.1672057 19.378508
## LK 6.778957e+01        0.16478915 1.29445871  1.9186975 1.6065781 10.473220
## SE 3.445684e+03        2.61847597 4.08274767  6.2624588 5.1726032 20.095525
## CH 3.602166e+03        2.28867018 3.98029809  6.3184688 5.1493834 18.623217
## TJ 3.406280e+02        0.78207686 0.44981821  0.5970683 0.5234433  3.021888
## TH 4.398768e+01        0.26030714 2.07568057  2.9874503 2.5315654 11.900893
## TG 5.006912e+01        0.42289247 0.23607069  0.3039569 0.2700138  2.869468
## TN 9.087605e+01        0.71183934 1.43186270  2.0076257 1.7197442  8.315679
## TR 1.928602e+03        1.73570816 1.25823610  2.0967233 1.6774797  8.483213
## UG 6.577232e+00        0.00000000 0.13547819  0.2760308 0.2057545  1.940987
## UA 4.837020e+02        1.18842716 2.37050281  5.6047403 3.9876216 16.434686
## AE 3.319399e+03        1.43897112 0.09768939  0.1638645 0.1307769  1.085001
## UK 4.019312e+03        2.75158819 4.13800199  5.9184161 5.0282090 18.395866
## US 5.195911e+03        2.48855760 3.10021646  4.6499908 3.8751036 15.807654
## UY 2.328009e+02        0.86794498 2.71338358  6.0761446 4.3947641 14.814520
## UZ 1.011367e+02        0.15375896 0.62822093  1.0455867 0.8369038  4.419138
## YE 8.947781e+00        0.45632576 0.28362786  0.4161354 0.3498816  2.876270
## ZM 6.091579e+01        0.14718638 0.15468651  0.3512552 0.2529708  2.099678
## ZW 9.141896e+00        0.10620013 0.24497323  0.5995746 0.4222739  2.939524
##    PoblaMid   PoblaData   PoblaDens  Mujeres  Urbano ExpectVida NeontlMort
## AF 54.32490  0.37172386  0.56937760 48.63585  25.495     62.701       37.1
## AL 68.58239  0.02866376  1.04612263 49.06309  60.319     76.601        6.5
## DZ 63.48882  0.42228429  0.17730075 49.48427  72.629     75.307       14.6
## AO 50.97470  0.30809762  0.24713052 50.53046  65.514     57.677       28.5
## AR 64.12128  0.44494502  0.16258510 51.23735  91.870     72.924        6.4
## AM 68.11276  0.02951776  1.03680225 52.95658  63.149     71.115        6.5
## AU 65.15291  0.24992369  0.03249129 50.19962  86.012     80.400        2.3
## AT 66.70049  0.08847037  1.07206927 50.82943  58.297     79.300        2.1
## AZ 70.43525  0.09942334  1.20265320 50.11575  55.680     70.128       11.2
## BD 67.13559  1.61356039 12.39579312 49.38730  36.632     70.409       17.1
## BY 68.28891  0.09485386  0.46728800 53.45605  78.595     69.300        1.3
## BE 64.15583  0.11422068  3.77214927 50.59332  98.001     79.000        2.0
## BZ 64.98378  0.00383071  0.16793994 50.19252  45.724     71.533        8.6
## BJ 54.29871  0.11485048  1.01853920 50.09820  47.312     59.633       31.3
## BT 68.22563  0.00754394  0.19777528 47.00264  40.895     70.822       16.4
## BO 61.73450  0.11353142  0.10480146 49.78340  69.425     68.173       14.3
## BA 68.76346  0.03323929  0.64920488 51.01054  48.245     74.622        4.1
## BW 61.66318  0.02254126  0.03977425 51.73076  69.446     65.790       24.5
## BR 69.74309  2.09469333  0.25061716 50.82992  86.569     71.804        8.1
## BN 72.10039  0.00428962  0.81396964 48.03618  77.629     74.454        5.5
## BG 64.38262  0.07024216  0.64703537 51.41409  75.008     71.300        3.6
## BF 52.64494  0.19751535  0.72191283 50.09548  29.358     59.981       24.7
## BI 52.25138  0.11175378  4.35178271 50.42199  13.032     59.092       21.7
## KH 64.22991  0.16249798  0.92056413 51.19798  23.388     67.062       14.4
## CM 54.63954  0.25216237  0.53343989 50.00349  56.374     57.235       26.6
## CA 66.89774  0.37058856  0.04075308 50.39153  81.411     80.288        3.4
## CF 52.87991  0.04666377  0.07490412 50.43647  41.364     50.152       41.2
## CO 68.44406  0.49648685  0.44748702 50.92577  80.778     74.124        7.8
## KM 57.45457  0.00832322  4.47244478 49.55870  28.965     62.210       31.6
## CG 55.55478  0.05244363  0.15356846 50.06560  66.916     62.546       20.3
## HR 65.04263  0.04089400  0.73077198 51.85262  56.947     74.900        2.6
## CZ 64.99252  0.10625695  1.37602888 50.80859  73.792     76.500        1.8
## DK 63.72878  0.05797446  1.38067302 50.27420  87.874     79.200        3.1
## DJ 65.89830  0.00958920  0.41368421 47.36679  77.777     64.000       31.7
## DO 64.94005  0.10627165  2.19978576 50.00780  81.074     70.609       19.4
## EC 64.81412  0.17084357  0.68788682 49.97063  63.821     73.833        7.2
## EG 60.97150  0.98423595  0.98873469 49.46997  42.704     69.453       11.2
## SV 64.58083  0.06420744  3.09881467 53.11400  72.023     68.006        6.7
## GQ 60.42552  0.01308974  0.46665740 44.45585  72.143     57.059       29.9
## EE 64.01657  0.01320884  0.30386105 52.85843  68.880     73.300        1.2
## ET 55.71589  1.09224559  1.09224559 49.97889  20.763     63.997       28.1
## FJ 65.03770  0.00883483  0.48357033 49.30050  56.248     65.594       10.9
## FI 62.13403  0.05518050  0.18156856 50.72076  85.382     78.600        1.0
## FR 62.00891  0.66987244  1.22338396 51.58424  80.444     79.500        2.5
## GA 59.41022  0.02119275  0.08224764 49.06862  89.370     63.879       21.0
## GM 53.14127  0.02280102  2.25306522 50.40213  61.270     60.088       26.3
## GE 65.33927  0.03731000  0.65275202 52.29124  58.632     68.980        5.9
## DE 64.91701  0.82927922  2.37370970 50.66037  77.312     78.600        2.2
## GH 59.33504  0.29767108  1.30821429 49.32583  56.060     62.437       23.9
## EL 64.27348  0.10727668  0.83224732 50.91620  79.058     78.900        2.6
## GT 60.75253  0.17247807  1.60953779 50.75935  51.054     70.830       12.3
## GN 53.22380  0.12414318  0.50522212 51.82111  36.140     60.040       31.1
## GY 65.33518  0.00779004  0.03957348 49.80802  26.606     66.605       18.2
## HT 61.80835  0.11123176  4.03598549 50.65221  55.278     61.139       26.0
## HN 63.56850  0.09587522  0.85687032 50.04934  57.096     72.573        9.6
## HU 66.43028  0.09768785  1.07906606 52.43243  71.351     72.600        2.3
## TD 50.39318  0.15477751  0.12291734 50.08490  23.059     52.315       34.2
## CL 68.71630  0.18729160  0.25189446 50.72703  87.564     77.333        4.9
## CN 71.20211 13.92730000  1.48348833 48.67937  59.152     74.315        4.3
## IN 66.76674 13.52617328  4.54938073 48.02354  34.030     68.000       22.7
## ID 67.59164  2.67663435  1.47752190 49.64388  55.325     69.156       12.7
## IR 69.33887  0.81800269  0.50222420 49.43913  74.898     75.217        8.9
## IQ 58.28983  0.38433600  0.88530570 49.40868  70.473     68.277       15.3
## IE 64.72778  0.04853506  0.70452983 50.42551  63.170     80.200        2.3
## IL 60.09777  0.08883800  4.10526802 50.29813  92.418     80.700        1.9
## IT 63.91920  0.60431283  2.05450748 51.37667  70.438     81.000        2.0
## JM 67.45326  0.02934855  2.70993075 50.33983  55.674     72.708       10.2
## JP 59.72678  1.26529100  3.47073458 51.15926  91.616     81.090        0.9
## JO 61.90802  0.09956011  1.12142498 49.38968  90.979     72.628        9.5
## KZ 64.14760  0.18276499  0.06769826 51.51148  57.428     68.720        5.6
## KE 57.87865  0.51393010  0.90299417 50.31602  27.030     63.539       19.6
## KW 75.91064  0.04137309  2.32172222 39.54817 100.000     74.584        4.5
## LV 63.96060  0.01926542  0.30983307 54.01017  68.142     69.900        2.0
## LB 66.90176  0.06848925  6.69494135 49.70581  88.593     77.031        4.3
## LS 62.38256  0.02108132  0.69437813 50.71690  28.153     49.837       34.9
## LR 55.62158  0.04818977  0.50030907 49.76829  51.151     61.911       24.5
## LY 67.28872  0.06678567  0.03795632 49.48379  80.102     69.672        6.4
## LT 65.41254  0.02789533  0.44531351 53.79196  67.679     69.500        2.1
## LU 69.93802  0.00607728  2.50093827 49.53926  90.981     80.100        1.4
## MG 56.34513  0.26262368  0.45139856 50.12397  37.191     64.728       20.6
## MW 53.45228  0.18143315  1.92440762 50.70152  16.937     60.155       22.4
## MY 69.33310  0.31528585  0.95962821 48.57852  76.036     73.903        4.3
## ML 49.94928  0.19077690  0.15635016 49.94100  42.356     57.718       32.7
## MR 56.77571  0.04403319  0.04272164 49.82266  53.672     62.831       33.5
## MU 70.73213  0.01265303  6.23301970 50.57711  40.793     71.300        9.2
## MX 66.21947  1.26190788  0.64914626 51.08928  80.156     72.046        7.5
## MD 72.67035  0.03545883  1.23519804 52.03556  42.629     67.439       11.9
## MN 65.50689  0.03170208  0.02040609 50.66954  68.445     65.465        8.7
## ME 66.81554  0.00622345  0.46271004 50.55901  66.813     74.205        1.7
## MA 65.78072  0.36029138  0.80728519 50.40385  62.453     74.948       13.8
## MZ 52.43844  0.29495962  0.37508535 51.47519  35.988     56.293       27.8
## MM 67.84431  0.53708395  0.82238615 51.80790  30.579     63.419       23.1
## NM 59.45372  0.02448255  0.02973746 51.55369  50.032     60.020       15.6
## NP 63.85806  0.28087871  1.95939107 54.53534  19.740     68.710       19.9
## NL 64.69565  0.17231017  5.11457910 50.22094  91.490     80.000        2.1
## NZ 64.69414  0.04885500  0.18554176 50.83777  86.538     80.000        3.5
## NI 64.55082  0.06465513  0.53727048 50.71271  58.522     70.551        9.4
## NE 47.42067  0.22442948  0.17717651 49.77100  16.425     60.485       25.2
## NO 65.40174  0.05314336  0.14554920 49.52463  82.248     80.900        1.5
## OM 75.36071  0.04829483  0.15604145 34.01408  84.539     75.645        5.1
## PK 60.41741  2.12215030  2.75289319 48.53807  36.666     66.041       42.0
## PA 64.83296  0.04176873  0.56186077 49.90538  67.709     75.060        8.5
## PY 64.12928  0.06956071  0.17508359 49.15161  61.585     72.041       10.7
## PE 66.12100  0.31989256  0.24991606 50.33776  77.907     73.612        7.3
## PH 63.91439  1.06651922  3.57688305 49.74166  46.907     66.971       13.5
## PL 67.42991  0.37978548  1.24035886 51.53071  60.058     73.900        2.7
## PT 64.58823  0.10281762  1.12239454 52.71196  65.211     78.100        2.1
## QA 85.08917  0.02781677  2.39593196 24.49529  99.135     78.830        3.5
## RO 66.12674  0.19473936  0.84639847 51.34374  53.998     71.700        3.4
## RW 57.08623  0.12301939  4.98659870 50.86106  17.211     66.187       15.9
## VC 67.86891  0.00110210  2.82589744 49.20878  52.198     70.075        9.7
## SA 71.64306  0.33699947  0.15676654 42.44585  83.844     73.671        3.7
## SN 53.85775  0.15854360  0.82347478 51.27733  47.192     65.242       20.6
## RS 65.96453  0.06982084  0.79831740 51.00252  56.092     73.600        3.4
## SL 55.97380  0.07650154  1.05987171 50.12127  42.055     53.049       32.8
## SG 76.25834  0.05638676 79.52998418 47.65813 100.000     80.700        1.1
## SK 68.92462  0.05447011  1.13290578 51.33833  53.726     73.800        2.8
## SI 65.37135  0.02067372  1.02639860 50.24521  54.541     78.200        1.2
## ZA 65.60251  0.57779622  0.47630120 50.69415  66.355     60.162       10.7
## KR 72.60812  0.51635256  5.29652104 49.91688  81.459     79.700        1.5
## ES 65.95449  0.46723749  0.93529058 50.89664  80.321     80.500        1.7
## LK 65.32978  0.21670000  3.45558922 51.96682  18.476     73.238        4.5
## SE 62.32269  0.10183175  0.25001043 49.94578  87.431     80.600        1.5
## CH 66.46583  0.08516543  2.15521378 50.42712  73.797     81.700        2.9
## TJ 60.19383  0.09100837  0.65572714 49.58384  27.134     68.493       15.0
## TH 71.01212  0.69428524  1.35897207 51.26870  49.949     72.977        5.0
## TG 55.79601  0.07889094  1.45046773 50.26805  41.702     59.644       24.9
## TN 67.51420  0.11565204  0.74441323 50.43715  68.945     74.297       11.5
## TR 66.86738  0.82319724  1.06960129 50.67781  75.143     74.149        5.5
## UG 51.12849  0.42723139  2.13061734 50.77608  23.774     60.270       19.9
## UA 67.75290  0.44622516  0.77029667 53.68775  69.352     67.020        5.2
## AE 84.31149  0.09630959  1.35609110 30.63669  86.522     76.966        4.0
## UK 63.92605  0.66488991  2.74827392 50.63527  83.398     79.400        2.6
## US 65.48331  3.27167434  0.35766089 50.52001  82.256     76.100        3.5
## UY 64.57750  0.03449299  0.19708028 51.72154  95.334     73.805        4.5
## UZ 66.89480  0.32955400  0.77469205 50.13736  50.478     69.250       11.6
## YE 57.50884  0.28498687  0.53977853 49.61166  36.642     64.413       27.0
## ZM 52.96418  0.17351822  0.23341479 50.49321  43.521     60.158       23.5
## ZW 54.65941  0.14439018  0.37324591 52.35675  32.209     59.105       20.9
##    DisMort Lesion EnfNoTrans EnfTrans         PBI Tuberculosis Diabetes
## AF    29.8   19.5       44.1     36.4   1.8351696        189.0      9.2
## AL    17.0    4.0       93.1      2.9  11.3351950         18.0      9.0
## DZ    14.2    9.5       75.7     14.8  14.1967389         69.0      6.7
## AO    16.5    9.2       27.4     63.4   6.7205961        355.0      4.5
## AR    15.8    6.5       77.6     15.9  20.0684923         27.0      5.9
## AM    22.3    3.9       93.3      2.8   8.3491802         31.0      6.1
## AU     9.1    5.9       89.5      4.6  45.7525548          6.6      5.6
## AT    11.4    5.2       92.2      2.6  48.9687140          7.1      6.6
## AZ    22.2    4.6       86.6      8.8  17.0906963         63.0      6.1
## BD    21.6    7.5       66.9     25.6   3.3061083        221.0      9.2
## BY    23.7    7.0       90.5      2.5  18.1721809         31.0      5.0
## BE    11.4    6.4       85.7      7.9  45.2631622          9.0      4.6
## BZ    22.1   13.2       67.4     19.4   8.0937796         30.0     17.1
## BJ    19.6   10.2       35.7     54.1   2.0675705         56.0      1.0
## BT    23.3   10.5       68.6     20.9   8.3417246        149.0     10.3
## BO    17.2   13.1       64.5     22.4   6.5317860        108.0      6.8
## BA    17.8    3.7       94.5      1.8  11.6971771         25.0      9.0
## BW    20.3    8.3       45.7     46.0  16.1336867        275.0      5.8
## BR    16.6   12.2       73.9     13.9  15.5847506         45.0     10.4
## BN    16.6    7.4       84.8      7.8  80.8004129         68.0     13.3
## BG    23.6    2.6       95.2      2.2  17.9465777         22.0      6.0
## BF    21.7   11.0       32.7     56.3   1.6709928         48.0      7.3
## BI    22.9   12.1       32.1     55.8   0.7568378        111.0      5.1
## KH    21.1   10.0       64.4     25.6   3.3333517        302.0      6.4
## CM    21.6   10.9       35.2     53.9   3.2930885        186.0      6.0
## CA     9.8    6.1       88.3      5.6  44.2264907          5.6      7.6
## CF    23.1   10.3       26.0     63.7   0.8532664        540.0      6.0
## CO    15.8   15.0       74.8     10.1  13.2116325         33.0      7.4
## KM    22.9   10.9       41.7     47.4   2.6420144         35.0     12.3
## CG    16.7    9.9       34.6     55.5   5.6636489        375.0      6.0
## HR    16.7    5.3       92.4      2.3  22.9922117          8.4      5.4
## CZ    15.0    4.7       89.9      5.4  32.7718057          5.4      7.0
## DK    11.3    3.8       89.7      6.5  48.5249925          5.4      8.3
## DJ    19.6   10.4       44.4     45.2   2.7442687        260.0      5.1
## DO    19.0   12.0       72.3     15.8  13.9050879         45.0      8.6
## EC    13.0   12.8       72.2     15.1  10.8759023         44.0      5.5
## EG    27.7    5.8       84.1     10.2  10.8110341         12.0     17.2
## SV    14.0   15.4       73.8     10.8   7.2348467         70.0      8.8
## GQ    22.0   10.7       35.9     53.4  30.5908474        201.0      6.0
## EE    17.0    4.5       92.7      2.8  28.8340001         13.0      4.2
## ET    18.3   11.7       39.3     49.0   1.5126247        151.0      4.3
## FJ    30.6    5.4       84.4     10.1   8.8754441         54.0     14.7
## FI    10.2    5.5       93.2      1.3  42.8552430          4.7      5.6
## FR    10.6    6.4       87.6      6.0  40.3515680          8.9      4.8
## GA    14.4    9.1       41.0     49.9  17.0778163        525.0      6.0
## GM    20.4   10.9       34.3     54.8   2.4051919        174.0      1.9
## GE    24.9    3.6       93.7      2.7   9.5834608         80.0      5.8
## DE    12.1    4.0       91.2      4.8  46.5762068          7.3     10.4
## GH    20.8    9.8       42.7     47.5   3.9219764        148.0      2.5
## EL    12.4    2.9       86.2     10.9  27.2065489          4.5      4.7
## GT    14.9   15.7       59.2     25.1   7.5159291         26.0     10.0
## GN    22.4    9.2       35.1     55.7   2.0022876        176.0      2.4
## GY    30.5   12.4       67.6     20.0   7.1987245         83.0     11.6
## HT    26.5   12.6       57.1     30.3   1.7058976        176.0      6.7
## HN    14.0   19.6       66.5     14.0   4.4336960         37.0      7.3
## HU    23.0    4.4       93.8      1.8  25.7573736          6.4      6.9
## TD    23.9    9.3       27.3     63.4   2.0073216        142.0      6.0
## CL    12.4    7.1       84.7      8.1  22.2523960         18.0      8.6
## CN    17.0    7.0       89.3      3.8  13.5313781         61.0      9.2
## IN    23.3   11.3       62.7     26.0   5.8366566        199.0     10.4
## ID    26.4    6.0       73.3     20.7  10.5772045        316.0      6.3
## IR    14.8   10.1       81.9      7.9  18.4501839         14.0      9.6
## IQ    21.3   28.4       54.7     16.8  15.8895142         42.0      8.8
## IE    10.3    4.3       90.6      5.1  59.3055780          7.0      3.2
## IL     9.6    4.1       85.8     10.0  34.5710854          4.0      9.7
## IT     9.5    3.8       91.4      4.9  37.7630816          7.0      5.0
## JM    14.7    8.7       80.0     11.2   8.5085469          2.9     11.3
## JP     8.4    4.8       82.4     12.7  39.1530060         14.0      5.6
## JO    19.2   10.9       78.4     10.7   9.2014965          5.0     12.7
## KZ    26.8    9.5       86.0      4.5  24.1030338         68.0      6.1
## KE    13.4    9.6       27.1     63.3   2.8839337        292.0      3.1
## KW    17.4   12.8       72.4     14.8  75.8307296         23.0     12.2
## LV    21.9    5.4       91.8      2.7  23.8146826         29.0      5.0
## LB    17.9    5.8       90.6      3.6  13.2450688         11.0     11.2
## LS    26.6    8.3       32.3     59.3   2.9043623        611.0      4.5
## LR    17.6   10.0       31.4     58.5   1.2563111        308.0      2.4
## LY    20.1   20.1       71.9      8.0  19.3396159         40.0     10.2
## LT    20.7    6.6       89.8      3.6  27.8087523         44.0      3.8
## LU    10.0    6.7       88.4      4.9 100.2191161          8.0      5.0
## MG    22.9   10.6       43.2     46.2   1.7044431        233.0      4.5
## MW    16.4    8.6       31.7     59.7   1.1836484        181.0      4.5
## MY    17.2    8.9       73.6     17.5  25.8714034         92.0     16.7
## ML    24.6    8.9       30.5     60.6   2.0131236         53.0      2.4
## MR    18.1    9.4       37.2     53.4   3.8172107         93.0      7.1
## MU    22.6    4.8       88.7      6.5  19.3987280         13.0     22.0
## MX    15.7   10.3       79.9      9.8  17.8555895         23.0     13.5
## MD    24.9    5.7       90.1      4.2   5.8933274         86.0      5.7
## MN    30.2   10.6       79.7      9.7  11.1277975        428.0      4.7
## ME    20.6    3.5       95.0      1.4  16.2734892         15.0      9.0
## MA    12.4    6.4       79.6     14.0   7.4795258         99.0      7.0
## MZ    18.4    7.8       26.9     65.3   1.2616857        551.0      3.3
## MM    24.2    8.6       67.8     23.6   5.0214146        338.0      3.9
## NM    21.3    9.8       40.9     49.3  10.2230967        524.0      4.5
## NP    21.8    8.8       66.2     25.0   2.4752041        151.0      7.2
## NL    11.2    5.2       89.6      5.2  49.9843155          5.3      5.4
## NZ    10.1    6.0       89.5      4.6  36.4995043          7.3      6.2
## NI    14.2   12.7       76.4     10.9   4.8837325         41.0     11.4
## NE    20.0   10.4       27.0     62.7   0.9278714         87.0      2.4
## NO     9.2    5.6       87.0      7.3  62.6503184          4.1      5.3
## OM    17.8   17.7       71.9     10.5  42.4790524          5.9     10.1
## PK    24.7    7.3       57.8     34.9   4.6501098        265.0     19.9
## PA    13.0    9.7       74.6     15.6  20.8839055         52.0      7.7
## PY    17.5   12.0       74.4     13.7  11.4029323         43.0      9.6
## PE    12.6   10.5       69.2     20.3  12.3515166        123.0      6.6
## PH    26.8    7.5       67.3     25.2   7.0082665        554.0      7.1
## PL    18.7    4.7       90.3      5.0  25.9904310         16.0      6.1
## PT    11.1    4.2       85.6     10.2  29.3390041         24.0      9.8
## QA    15.3   25.9       68.9      5.2 123.2139364         31.0     15.6
## RO    21.4    3.6       92.2      4.2  21.6182719         68.0      6.9
## RW    18.2   13.5       44.0     42.4   1.7889788         59.0      5.1
## VC    23.2    5.6       81.0     13.4  10.9317631          6.3     11.6
## SA    16.4   16.3       73.2     10.6  51.5878305         10.0     15.8
## SN    18.1   12.2       42.1     45.7   3.1310271        118.0      2.4
## RS    19.1    3.0       94.6      2.4  14.9080484         17.0      9.0
## SL    30.5    8.9       33.2     57.9   1.4959394        298.0      2.4
## SG     9.3    3.7       73.6     22.7  86.0684237         47.0      5.5
## SK    17.2    6.0       89.2      4.8  29.0915206          5.8      6.5
## SI    12.7    6.6       88.4      5.0  31.7402860          5.3      5.9
## ZA    26.2    9.1       51.3     39.6  12.8666891        520.0     12.7
## KR     7.8   10.0       79.8     10.1  34.6370853         66.0      6.9
## ES     9.9    3.5       91.4      5.1  34.5888453          9.4      6.9
## LK    17.4    9.7       82.8      7.5  11.1185327         64.0     10.7
## SE     9.1    4.9       89.9      5.2  47.6285919          5.5      4.8
## CH     8.6    6.1       89.6      4.3  61.3146089          6.4      5.7
## TJ    25.3    7.6       69.2     23.2   2.7293337         84.0      6.1
## TH    14.5   10.2       74.0     15.8  15.8571484        153.0      7.0
## TG    23.6   10.7       37.6     51.7   1.4956789         36.0      2.4
## TN    16.1    6.4       85.8      7.8  11.2660561         35.0      8.5
## TR    16.1    6.2       89.4      4.4  23.5212137         16.0     11.1
## UG    21.9   12.7       32.9     54.5   1.8103287        200.0      2.5
## UA    24.7    5.0       91.0      4.1   8.4417522         80.0      6.1
## AE    16.8   16.8       76.8      6.5  65.5180899          1.0     16.3
## UK    10.9    3.5       88.8      7.7  41.1611271          8.0      3.9
## US    14.6    6.6       88.3      5.2  55.0581658          3.0     10.8
## UY    16.7    7.5       84.9      7.6  20.4797528         33.0      7.3
## UZ    24.5    6.0       83.7     10.3   6.8361061         70.0      6.5
## YE    30.6   14.7       56.6     28.7   3.5314239         48.0      5.4
## ZM    17.9   10.2       29.2     60.6   3.8025013        346.0      4.5
## ZW    19.3   12.3       33.0     54.6   2.5606953        210.0      1.8
##       Medicos      Camas ImmunSaramp TempMarzo HipTen.H HipTen.M HipTen BCG
## AF 0.24009091  0.4363636          64      7.60     18.6     19.8  19.20   1
## AL 1.21237143  2.9375000          94      6.04     41.6     39.4  40.50   1
## DZ 1.31202500  1.9000000          80     17.91     22.3     23.0  22.65   1
## AO 0.17300000  0.8000000          50     22.78     31.1     25.2  28.15   1
## AR 3.57165000  4.6000000          94     17.51     41.8     32.9  37.35   1
## AM 3.06122500  4.0200000          95     -0.57     41.2     43.4  42.30   1
## AU 3.27402222  3.8783333          95     25.37     34.8     32.8  33.80   1
## AT 4.66801111  7.7000000          94      1.42     44.9     38.8  41.85   1
## AZ 3.58736250  6.4666667          96      4.97     25.9     31.4  28.65   1
## BD 0.39047692  0.5750000          97     25.42     15.2     17.4  16.30   1
## BY 4.22154000 11.2000000          97     -0.69     43.9     45.5  44.70   1
## BE 2.79814444  6.5500000          96      5.23     39.4     35.0  37.20   0
## BZ 0.96785000  1.1600000          97     24.45     24.3     25.1  24.70   1
## BJ 0.11874000  0.5000000          71     30.14     39.9     40.3  40.10   1
## BT 0.26431111  1.7333333          97      5.68     19.7     20.4  20.05   1
## BO 0.71612500  1.1000000          89     22.07     27.4     27.2  27.30   1
## BA 1.77503333  3.3666667          68      4.01     45.6     43.2  44.40   1
## BW 0.35774444  2.0000000          97     24.30     42.2     41.0  41.60   1
## BR 1.84654000  2.3285714          84     25.49     28.1     38.5  33.30   1
## BN 1.38302727  2.7542857          99     25.92     29.1     32.0  30.55   1
## BG 3.80887500  6.4888889          93      4.70     45.5     46.5  46.00   1
## BF 0.04237143  0.6500000          88     30.63      9.0     12.4  10.70   1
## BI 0.04885000  1.1333333          88     20.43     32.6     30.8  31.70   1
## KH 0.24971429  0.7600000          84     27.93     25.5     24.1  24.80   1
## CM 0.07534000  1.4000000          71     26.27     18.5     17.9  18.20   1
## CA 2.32944444  3.0714286          90    -18.72     22.7     23.9  23.30   0
## CF 0.04973333  1.1000000          49     26.96     33.6     33.2  33.40   1
## CO 1.70129231  1.3166667          95     25.14     28.7     28.7  28.70   1
## KM 0.18323333  2.2000000          90     25.20     43.6     41.5  42.55   1
## CG 0.12880000  1.6000000          75     25.60     18.2     19.3  18.75   1
## HR 2.80697000  5.6354545          93      5.83     43.7     38.5  41.10   1
## CZ 3.70292500  7.0100000          96      2.80     48.2     42.6  45.40   1
## DK 3.56958333  3.4111111          95      2.24     39.9     34.6  37.25   1
## DJ 0.21512500  1.4571429          86     25.75     19.8     22.3  21.05   1
## DO 1.37733333  1.4375000          95     22.88     27.4     29.3  28.35   1
## EC 1.90360000  1.5285714          83     21.93     28.5     28.6  28.55   1
## EG 1.73048750  1.5100000          94     17.83     17.1     23.9  20.50   1
## SV 1.65428000  0.9900000          81     25.70     27.1     30.0  28.55   1
## GQ 0.40000000  2.0666667          30     25.04     28.2     25.2  26.70   1
## EE 3.34070000  5.3600000          87     -2.35     45.5     42.9  44.20   1
## ET 0.03620000  1.7500000          61     23.48     27.5     22.2  24.85   1
## FJ 0.58042000  2.1475000          94     25.00     27.6     25.0  26.30   1
## FI 3.14394000  5.9600000          96     -6.09     54.6     46.6  50.60   1
## FR 3.26508571  6.9222222          90      6.37     38.0     38.5  38.25   1
## GA 0.36110000  3.2000000          59     26.14     44.7     43.6  44.15   1
## GM 0.09858333  1.0000000          91     27.32     33.0     30.4  31.70   1
## GE 4.62520000  3.1222222          98      0.72     40.2     41.0  40.60   1
## DE 3.84022000  8.2555556          97      3.87     35.6     34.2  34.90   1
## GH 0.12015714  0.9000000          92     29.52     41.5     41.4  41.45   1
## EL 5.71011111  4.6555556          97      8.19     39.2     36.4  37.80   1
## GT 0.62945000  0.6250000          87     22.94     25.6     26.5  26.05   1
## GN 0.09196667  0.3000000          48     27.63     40.6     41.0  40.80   1
## GY 0.56823333  2.1600000          98     25.65     26.2     26.3  26.25   1
## HT 0.18585000  1.0000000          69     23.47     25.5     26.7  26.10   1
## HN 0.57160000  0.7285714          89     23.38     25.5     25.7  25.60   1
## HU 3.24861667  7.3000000          99      5.44     46.2     47.4  46.80   1
## TD 0.04200000  0.4000000          37     26.55     32.7     31.3  32.00   1
## CL 1.03645000  2.1714286          93     10.87     31.7     28.2  29.95   1
## CN 1.57320000  3.4066667          99      0.49     35.7     32.3  34.00   1
## IN 0.67390833  0.8000000          90     23.45     25.8     29.2  27.50   1
## ID 0.24655000  0.9000000          75     25.79     25.0     24.7  24.85   1
## IR 0.98516000  1.4400000          99     11.33     26.4     25.9  26.15   1
## IQ 0.72533333  1.3100000          83     15.12     24.8     23.9  24.35   1
## IE 2.81283000  3.9555556          92      6.00     46.0     32.2  39.10   1
## IL 3.38105556  3.5333333          98     14.96     33.8     40.1  36.95   1
## IT 3.94927778  3.6875000          93      6.52     45.7     36.5  41.10   0
## JM 0.54227143  1.7571429          89     23.44     30.4     33.6  32.00   1
## JP 2.27806667 13.7960000          97      2.52     46.7     37.5  42.10   1
## JO 2.24923636  1.7818182          92     13.08     29.3     26.3  27.80   1
## KZ 3.60587000  7.4111111          99     -3.96     33.9     33.1  33.50   1
## KE 0.18122857  1.4000000          89     26.10     34.7     36.8  35.75   1
## KW 2.16873636  1.9800000          99     19.23     24.5     26.5  25.50   1
## LV 3.38225000  6.8111111          98     -1.37     44.7     36.7  40.70   1
## LB 2.55344444  3.3800000          82     10.39     30.6     29.6  30.10   0
## LS 0.06760000  1.3000000          90     15.13     17.5     35.0  26.25   1
## LR 0.02466667  0.7500000          91     26.44     39.9     40.3  40.10   1
## LY 1.98748333  3.6700000          97     17.76     23.4     23.6  23.50   1
## LT 4.08613000  7.1666667          92     -0.47     43.5     40.5  42.00   1
## LU 2.85313000  5.3727273          99      4.70     48.5     37.1  42.80   1
## MG 0.17360000  0.2500000          62     23.99     33.1     32.2  32.65   1
## MW 0.01767500  1.2000000          87     23.25     33.4     32.6  33.00   1
## MY 1.24358000  1.8312500          96     25.19     26.5     25.2  25.85   1
## ML 0.09575714  0.3333333          70     27.34     32.6     32.1  32.35   1
## MR 0.14066667  0.4000000          78     25.29     26.3     29.5  27.90   1
## MU 1.45775556  3.2333333          99     25.48     49.0     48.4  48.70   1
## MX 2.03156667  1.5888889          97     18.44     29.3     28.5  28.90   1
## MD 2.62560000  6.1666667          93      2.93     43.3     42.2  42.75   1
## MN 2.96358889  6.3266667          99     -8.80     37.3     30.6  33.95   1
## ME 2.08927778  4.0125000          58      2.53     43.4     41.9  42.65   1
## MA 0.63655000  0.9800000          99     13.09     33.5     33.0  33.25   1
## MZ 0.04688750  0.7666667          85     25.48     36.1     31.5  33.80   1
## MM 0.53712000  0.7500000          93     22.74     24.7     25.5  25.10   1
## NM 0.37305000  3.0000000          82     22.67     43.2     41.9  42.55   1
## NP 0.57297500  2.6500000          91     10.02     21.6     22.1  21.85   1
## NL 3.40828889  4.6000000          93      4.95     39.1     33.8  36.45   0
## NZ 2.70726000  2.5500000          92     13.60     36.4     30.9  33.65   1
## NI 0.74390000  0.9222222          99     24.95     25.4     26.2  25.80   1
## NE 0.03340000  0.3000000          77     26.01     33.8     32.4  33.10   1
## NO 4.21626667  4.2555556          96     -5.49     38.2     33.0  35.60   1
## OM 1.95183077  1.8600000          99     23.40     22.2     26.2  24.20   1
## PK 0.84727500  0.7200000          76     16.01     20.1     21.1  20.60   1
## PA 1.43397000  2.2888889          98     25.44     27.7     24.6  26.15   1
## PY 0.96393333  1.2857143          93     25.84     27.8     27.2  27.50   1
## PE 1.18280000  1.4875000          85     19.99     16.2     17.8  17.00   1
## PH 1.25186667  0.5833333          67     25.13     28.0     23.5  25.75   1
## PL 2.19399000  6.5222222          93      2.65     40.9     35.0  37.95   1
## PT 3.79097273  3.4222222          99     11.33     38.6     35.7  37.15   1
## QA 2.47302500  1.6400000          99     21.78     26.8     36.2  31.50   1
## RO 2.40146667  6.4555556          90      3.40     39.1     46.7  42.90   1
## RW 0.09168889  1.6000000          99     19.25     32.2     30.4  31.30   1
## VC 0.65870000  3.3000000          99     25.90     27.9     30.7  29.30   1
## SA 2.25626250  2.2500000          98     20.60     25.6     27.0  26.30   1
## SN 0.14020000  0.2000000          82     28.47     42.3     40.4  41.35   1
## RS 2.42901111  5.6125000          92      4.91     43.5     41.6  42.55   1
## SL 0.02050000  0.4000000          80     27.65     39.8     39.7  39.75   1
## SG 1.76859091  2.7457143          95     28.62     25.7     22.1  23.90   1
## SK 3.11790000  6.3400000          96      2.40     39.5     33.7  36.60   1
## SI 2.59476000  4.6555556          93      3.38     42.5     37.2  39.85   1
## ZA 0.76988750  2.8000000          70     21.10     47.0     45.9  46.45   1
## KR 2.05984615 10.6080000          98      3.66     28.6     23.5  26.05   1
## ES 4.15920000  3.1888889          97      8.60     44.0     37.1  40.55   1
## LK 0.77147000  3.5500000          99     27.03     23.0     25.8  24.40   1
## SE 4.03310000  2.7666667          97     -4.98     41.8     37.0  39.40   0
## CH 4.00455000  5.1222222          96      0.09     38.6     31.7  35.15   1
## TJ 1.78443333  5.2666667          98     -3.08     26.3     25.4  25.85   1
## TH 0.40473333  2.1000000          96     27.38     23.4     23.8  23.60   1
## TG 0.08897500  0.8000000          85     29.31     39.5     40.1  39.80   1
## TN 1.16097143  2.0545455          96     14.21     20.9     25.2  23.05   1
## TR 1.65210000  2.4555556          96      4.77     38.3     35.1  36.70   1
## UG 0.10457500  0.7500000          86     23.68     24.6     32.7  28.65   1
## UA 3.35196667  8.9222222          91      1.18     47.7     50.6  49.15   1
## AE 1.70489167  1.6777778          99     22.61     17.9     19.2  18.55   1
## UK 2.75287500  3.2111111          92      4.66     31.7     29.9  30.80   1
## US 2.52747500  3.0333333          92      0.06     31.1     31.8  31.45   0
## UY 4.16766000  2.5250000          97     21.26     37.5     38.8  38.15   1
## UZ 2.50781250  4.5888889          96      5.38     27.1     27.7  27.40   1
## YE 0.30986667  0.6900000          64     20.82      9.6     12.4  11.00   1
## ZM 0.08791250  1.9500000          94     22.90     27.2     27.4  27.30   1
## ZW 0.06561250  2.3500000          88     22.92     33.2     32.0  32.60   1
##    BCGf Tiempo
## AF  Yes     56
## AL  Yes     69
## DZ  Yes     57
## AO  Yes     82
## AR  Yes     64
## AM  Yes     61
## AU  Yes     25
## AT  Yes     57
## AZ  Yes     60
## BD  Yes     69
## BY  Yes     59
## BE   No     35
## BZ  Yes     84
## BJ  Yes     77
## BT  Yes     66
## BO  Yes     72
## BA  Yes     66
## BW  Yes     92
## BR  Yes     57
## BN  Yes     70
## BG  Yes     68
## BF  Yes     71
## BI  Yes     92
## KH  Yes     28
## CM  Yes     67
## CA   No     26
## CF  Yes     76
## CO  Yes     67
## KM  Yes    123
## CG  Yes     76
## HR  Yes     57
## CZ  Yes     62
## DK  Yes     58
## DJ  Yes     79
## DO  Yes     62
## EC  Yes     61
## EG  Yes     46
## SV  Yes     79
## GQ  Yes     75
## EE  Yes     59
## ET  Yes     74
## FJ  Yes     80
## FI  Yes     30
## FR  Yes     25
## GA  Yes     73
## GM  Yes     78
## GE  Yes     58
## DE  Yes     28
## GH  Yes     73
## EL  Yes     58
## GT  Yes     75
## GN  Yes     74
## GY  Yes     73
## HT  Yes     80
## HN  Yes     72
## HU  Yes     65
## TD  Yes     80
## CL  Yes     64
## CN  Yes      0
## IN  Yes     30
## ID  Yes     62
## IR  Yes     51
## IQ  Yes     56
## IE  Yes     61
## IL  Yes     53
## IT   No     31
## JM  Yes     72
## JP  Yes     15
## JO  Yes     63
## KZ  Yes     75
## KE  Yes     74
## KW  Yes     55
## LV  Yes     63
## LB   No     53
## LS  Yes    136
## LR  Yes     77
## LY  Yes     85
## LT  Yes     59
## LU  Yes     61
## MG  Yes     81
## MW  Yes     94
## MY  Yes     25
## ML  Yes     86
## MR  Yes     75
## MU  Yes     80
## MX  Yes     60
## MD  Yes     68
## MN  Yes     70
## ME  Yes     78
## MA  Yes     63
## MZ  Yes     83
## MM  Yes     84
## NM  Yes     75
## NP  Yes     25
## NL   No     59
## NZ  Yes     59
## NI  Yes     79
## NE  Yes     81
## NO  Yes     58
## OM  Yes     56
## PK  Yes     58
## PA  Yes     70
## PY  Yes     68
## PE  Yes     67
## PH  Yes     30
## PL  Yes     64
## PT  Yes     63
## QA  Yes     61
## RO  Yes     58
## RW  Yes     75
## VC  Yes     73
## SA  Yes     63
## SN  Yes     63
## RS  Yes     67
## SL  Yes     92
## SG  Yes     24
## SK  Yes     67
## SI  Yes     65
## ZA  Yes     66
## KR  Yes     20
## ES  Yes     32
## LK  Yes     28
## SE   No     32
## CH  Yes     57
## TJ  Yes    122
## TH  Yes     13
## TG  Yes     67
## TN  Yes     63
## TR  Yes     72
## UG  Yes     82
## UA  Yes     64
## AE  Yes     27
## UK  Yes     31
## US   No     21
## UY  Yes     75
## UZ  Yes     76
## YE  Yes    101
## ZM  Yes     79
## ZW  Yes     81
attach(covid)

Una vez que cargamos el archivo podemos comenzar a explorar los datos.

glimpse(covid)
## Rows: 139
## Columns: 35
## $ geoId             <chr> "AF", "AL", "DZ", "AO", "AR", "AM", "AU", "AT", "AZ"…
## $ CntrName          <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Arge…
## $ muertes           <int> 227, 33, 623, 4, 500, 98, 103, 645, 54, 544, 214, 93…
## $ casos             <int> 12456, 1050, 8857, 71, 13920, 7774, 7139, 16515, 456…
## $ muertes.permil    <dbl> 6.10668360, 11.51279525, 14.75309441, 0.12982898, 11…
## $ casos.permil      <dbl> 335.087449, 366.316213, 209.740220, 2.304464, 312.84…
## $ l10muertes.permil <dbl> 0.85166698, 1.09735434, 1.19736588, 0.05301271, 1.08…
## $ Hombres80         <dbl> 0.2333258, 2.4480787, 1.1817555, 0.2075326, 1.774818…
## $ Mujeres80         <dbl> 0.3209823, 3.0339539, 1.3602137, 0.3372488, 3.447532…
## $ Pobla80           <dbl> 0.2771541, 2.7410163, 1.2709846, 0.2723907, 2.611175…
## $ Pobla65           <dbl> 2.584927, 13.744736, 6.362497, 2.216374, 11.117789, …
## $ PoblaMid          <dbl> 54.32490, 68.58239, 63.48882, 50.97470, 64.12128, 68…
## $ PoblaData         <dbl> 0.37172386, 0.02866376, 0.42228429, 0.30809762, 0.44…
## $ PoblaDens         <dbl> 0.56937760, 1.04612263, 0.17730075, 0.24713052, 0.16…
## $ Mujeres           <dbl> 48.63585, 49.06309, 49.48427, 50.53046, 51.23735, 52…
## $ Urbano            <dbl> 25.495, 60.319, 72.629, 65.514, 91.870, 63.149, 86.0…
## $ ExpectVida        <dbl> 62.701, 76.601, 75.307, 57.677, 72.924, 71.115, 80.4…
## $ NeontlMort        <dbl> 37.1, 6.5, 14.6, 28.5, 6.4, 6.5, 2.3, 2.1, 11.2, 17.…
## $ DisMort           <dbl> 29.8, 17.0, 14.2, 16.5, 15.8, 22.3, 9.1, 11.4, 22.2,…
## $ Lesion            <dbl> 19.5, 4.0, 9.5, 9.2, 6.5, 3.9, 5.9, 5.2, 4.6, 7.5, 7…
## $ EnfNoTrans        <dbl> 44.1, 93.1, 75.7, 27.4, 77.6, 93.3, 89.5, 92.2, 86.6…
## $ EnfTrans          <dbl> 36.4, 2.9, 14.8, 63.4, 15.9, 2.8, 4.6, 2.6, 8.8, 25.…
## $ PBI               <dbl> 1.8351696, 11.3351950, 14.1967389, 6.7205961, 20.068…
## $ Tuberculosis      <dbl> 189.0, 18.0, 69.0, 355.0, 27.0, 31.0, 6.6, 7.1, 63.0…
## $ Diabetes          <dbl> 9.2, 9.0, 6.7, 4.5, 5.9, 6.1, 5.6, 6.6, 6.1, 9.2, 5.…
## $ Medicos           <dbl> 0.24009091, 1.21237143, 1.31202500, 0.17300000, 3.57…
## $ Camas             <dbl> 0.4363636, 2.9375000, 1.9000000, 0.8000000, 4.600000…
## $ ImmunSaramp       <int> 64, 94, 80, 50, 94, 95, 95, 94, 96, 97, 97, 96, 97, …
## $ TempMarzo         <dbl> 7.60, 6.04, 17.91, 22.78, 17.51, -0.57, 25.37, 1.42,…
## $ HipTen.H          <dbl> 18.6, 41.6, 22.3, 31.1, 41.8, 41.2, 34.8, 44.9, 25.9…
## $ HipTen.M          <dbl> 19.8, 39.4, 23.0, 25.2, 32.9, 43.4, 32.8, 38.8, 31.4…
## $ HipTen            <dbl> 19.20, 40.50, 22.65, 28.15, 37.35, 42.30, 33.80, 41.…
## $ BCG               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1…
## $ BCGf              <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Ye…
## $ Tiempo            <int> 56, 69, 57, 82, 64, 61, 25, 57, 60, 69, 59, 35, 84, …

Con esto podemos ver que tenemos 31 variables numericas (dbl) y el resto con caracteres.

Vamos a utilizar describe(), que para las variables numericas nos muestra los estadísticos descriptivos, la cantidad de observaciones, los valores perdidos. Para las variables categóricas, muestra la frecuencia, las proporciones y los valores perdidos.

describe(covid)
##                   vars   n     mean        sd  median  trimmed     mad    min
## geoId*               1 139    70.00     40.27   70.00    70.00   51.89   1.00
## CntrName*            2 139    70.00     40.27   70.00    70.00   51.89   1.00
## muertes              3 139  2518.09  10218.47   66.00   411.64   96.37   0.00
## casos                4 139 37620.98 153435.34 3333.00 11417.52 4879.24   2.00
## muertes.permil       5 139    52.74    126.11    6.07    19.87    8.79   0.00
## casos.permil         6 139  1133.63   2050.18  277.58   715.48  369.85   0.95
## l10muertes.permil    7 139     1.03      0.76    0.85     0.97    0.83   0.00
## Hombres80            8 139     1.60      1.46    1.08     1.40    1.22   0.09
## Mujeres80            9 139     2.64      2.56    1.44     2.29    1.58   0.16
## Pobla80             10 139     2.12      2.00    1.27     1.86    1.40   0.13
## Pobla65             11 139     9.22      6.65    6.45     8.63    5.59   1.09
## PoblaMid            12 139    63.58      6.34   64.70    63.68    4.39  47.42
## PoblaData           13 139     0.49      1.68    0.11     0.19    0.12   0.00
## PoblaDens           14 139     1.88      6.84    0.81     1.02    0.83   0.02
## Mujeres             15 139    49.89      3.53   50.40    50.39    0.98  24.50
## Urbano              16 139    60.83     21.89   62.45    61.62   24.80  13.03
## ExpectVida          17 139    70.06      7.48   71.11    70.45    7.98  49.84
## NeontlMort          18 139    12.30     10.61    8.70    11.04    9.49   0.90
## DisMort             19 139    18.30      5.60   17.90    18.18    6.08   7.80
## Lesion              20 139     8.83      4.33    8.70     8.35    4.00   2.60
## EnfNoTrans          21 139    69.39     22.14   74.80    71.22   21.94  26.00
## EnfTrans            22 139    21.78     20.44   11.20    19.50   11.27   1.30
## PBI                 23 139    20.09     21.58   12.35    16.42   14.24   0.76
## Tuberculosis        24 139   106.47    139.78   45.00    77.53   56.34   1.00
## Diabetes            25 139     7.43      3.77    6.50     7.04    2.97   1.00
## Medicos             26 139     1.65      1.42    1.38     1.54    1.74   0.02
## Camas               27 139     2.98      2.47    2.17     2.65    1.85   0.20
## ImmunSaramp         28 139    88.21     13.22   93.00    90.77    7.41  30.00
## TempMarzo           29 139    15.38     11.10   19.99    16.15   10.23 -18.72
## HipTen.H            30 139    32.80      9.25   32.60    32.96   10.53   9.00
## HipTen.M            31 139    31.98      7.85   32.00    31.93    9.19  12.40
## HipTen              32 139    32.39      8.33   32.00    32.50    9.27  10.70
## BCG                 33 139     0.95      0.22    1.00     1.00    0.00   0.00
## BCGf*               34 139     1.95      0.22    2.00     2.00    0.00   1.00
## Tiempo              35 139    63.15     21.33   65.00    63.98   13.34   0.00
##                          max      range  skew kurtosis       se
## geoId*                139.00     138.00  0.00    -1.23     3.42
## CntrName*             139.00     138.00  0.00    -1.23     3.42
## muertes            100442.00  100442.00  7.11    59.97   866.72
## casos             1699933.00 1699931.00  9.32    96.46 13014.22
## muertes.permil        819.82     819.82  3.64    14.29    10.70
## casos.permil        17596.22   17595.27  4.33    28.30   173.89
## l10muertes.permil       2.91       2.91  0.55    -0.51     0.06
## Hombres80               6.17       6.08  1.05     0.27     0.12
## Mujeres80              10.56      10.39  1.00    -0.20     0.22
## Pobla80                 8.36       8.23  0.99    -0.12     0.17
## Pobla65                27.58      26.49  0.69    -0.90     0.56
## PoblaMid               85.09      37.67  0.00     0.89     0.54
## PoblaData              13.93      13.93  7.12    52.89     0.14
## PoblaDens              79.53      79.51 10.57   116.79     0.58
## Mujeres                54.54      30.04 -4.69    26.42     0.30
## Urbano                100.00      86.97 -0.29    -0.84     1.86
## ExpectVida             81.70      31.86 -0.48    -0.49     0.63
## NeontlMort             42.00      41.10  0.86    -0.38     0.90
## DisMort                30.60      22.80  0.14    -0.68     0.47
## Lesion                 28.40      25.80  1.43     3.49     0.37
## EnfNoTrans             95.20      69.20 -0.68    -1.02     1.88
## EnfTrans               65.30      64.00  0.89    -0.78     1.73
## PBI                   123.21     122.46  1.89     4.36     1.83
## Tuberculosis          611.00     610.00  1.86     2.86    11.86
## Diabetes               22.00      21.00  1.20     1.74     0.32
## Medicos                 5.71       5.69  0.53    -0.87     0.12
## Camas                  13.80      13.60  1.47     2.51     0.21
## ImmunSaramp            99.00      69.00 -2.00     4.13     1.12
## TempMarzo              30.63      49.35 -0.55    -0.89     0.94
## HipTen.H               54.60      45.60 -0.09    -0.73     0.78
## HipTen.M               50.60      38.20  0.05    -0.53     0.67
## HipTen                 50.60      39.90 -0.06    -0.65     0.71
## BCG                     1.00       1.00 -4.07    14.65     0.02
## BCGf*                   2.00       1.00 -4.07    14.65     0.02
## Tiempo                136.00     136.00 -0.23     1.25     1.81
  summary = summary(covid)
  summary
##     geoId             CntrName            muertes           casos          
##  Length:139         Length:139         Min.   :     0   Min.   :      2.0  
##  Class :character   Class :character   1st Qu.:    12   1st Qu.:    725.5  
##  Mode  :character   Mode  :character   Median :    66   Median :   3333.0  
##                                        Mean   :  2518   Mean   :  37621.0  
##                                        3rd Qu.:   548   3rd Qu.:  20625.0  
##                                        Max.   :100442   Max.   :1699933.0  
##  muertes.permil     casos.permil       l10muertes.permil   Hombres80      
##  Min.   :  0.000   Min.   :    0.949   Min.   :0.0000    Min.   :0.08737  
##  1st Qu.:  2.214   1st Qu.:   70.054   1st Qu.:0.5062    1st Qu.:0.37722  
##  Median :  6.074   Median :  277.583   Median :0.8497    Median :1.07801  
##  Mean   : 52.735   Mean   : 1133.633   Mean   :1.0282    Mean   :1.59580  
##  3rd Qu.: 33.787   3rd Qu.: 1596.139   3rd Qu.:1.5414    3rd Qu.:2.57442  
##  Max.   :819.817   Max.   :17596.220   Max.   :2.9142    Max.   :6.16625  
##    Mujeres80          Pobla80          Pobla65          PoblaMid    
##  Min.   : 0.1639   Min.   :0.1308   Min.   : 1.085   Min.   :47.42  
##  1st Qu.: 0.5983   1st Qu.:0.4885   1st Qu.: 3.284   1st Qu.:60.15  
##  Median : 1.4421   Median :1.2710   Median : 6.450   Median :64.70  
##  Mean   : 2.6441   Mean   :2.1200   Mean   : 9.223   Mean   :63.58  
##  3rd Qu.: 4.5155   3rd Qu.:3.7069   3rd Qu.:14.920   3rd Qu.:66.90  
##  Max.   :10.5564   Max.   :8.3613   Max.   :27.576   Max.   :85.09  
##    PoblaData           PoblaDens           Mujeres          Urbano      
##  Min.   : 0.001102   Min.   : 0.02041   Min.   :24.50   Min.   : 13.03  
##  1st Qu.: 0.047427   1st Qu.: 0.33375   1st Qu.:49.72   1st Qu.: 44.62  
##  Median : 0.111232   Median : 0.80729   Median :50.40   Median : 62.45  
##  Mean   : 0.485381   Mean   : 1.87767   Mean   :49.89   Mean   : 60.83  
##  3rd Qu.: 0.333277   3rd Qu.: 1.46399   3rd Qu.:51.01   3rd Qu.: 78.83  
##  Max.   :13.927300   Max.   :79.52998   Max.   :54.54   Max.   :100.00  
##    ExpectVida      NeontlMort       DisMort          Lesion      
##  Min.   :49.84   Min.   : 0.90   Min.   : 7.80   Min.   : 2.600  
##  1st Qu.:64.57   1st Qu.: 3.40   1st Qu.:14.30   1st Qu.: 5.650  
##  Median :71.11   Median : 8.70   Median :17.90   Median : 8.700  
##  Mean   :70.06   Mean   :12.30   Mean   :18.30   Mean   : 8.827  
##  3rd Qu.:75.14   3rd Qu.:20.45   3rd Qu.:22.35   3rd Qu.:10.700  
##  Max.   :81.70   Max.   :42.00   Max.   :30.60   Max.   :28.400  
##    EnfNoTrans       EnfTrans          PBI            Tuberculosis  
##  Min.   :26.00   Min.   : 1.30   Min.   :  0.7568   Min.   :  1.0  
##  1st Qu.:48.50   1st Qu.: 5.20   1st Qu.:  4.1778   1st Qu.: 13.0  
##  Median :74.80   Median :11.20   Median : 12.3515   Median : 45.0  
##  Mean   :69.39   Mean   :21.78   Mean   : 20.0941   Mean   :106.5  
##  3rd Qu.:88.75   3rd Qu.:38.00   3rd Qu.: 28.3214   3rd Qu.:150.0  
##  Max.   :95.20   Max.   :65.30   Max.   :123.2139   Max.   :611.0  
##     Diabetes         Medicos            Camas         ImmunSaramp   
##  Min.   : 1.000   Min.   :0.01767   Min.   : 0.200   Min.   :30.00  
##  1st Qu.: 5.050   1st Qu.:0.28709   1st Qu.: 1.147   1st Qu.:85.00  
##  Median : 6.500   Median :1.38303   Median : 2.171   Median :93.00  
##  Mean   : 7.425   Mean   :1.65391   Mean   : 2.980   Mean   :88.21  
##  3rd Qu.: 9.200   3rd Qu.:2.77551   3rd Qu.: 3.984   3rd Qu.:97.00  
##  Max.   :22.000   Max.   :5.71011   Max.   :13.796   Max.   :99.00  
##    TempMarzo         HipTen.H        HipTen.M         HipTen     
##  Min.   :-18.72   Min.   : 9.00   Min.   :12.40   Min.   :10.70  
##  1st Qu.:  4.93   1st Qu.:25.85   1st Qu.:25.85   1st Qu.:26.07  
##  Median : 19.99   Median :32.60   Median :32.00   Median :32.00  
##  Mean   : 15.38   Mean   :32.80   Mean   :31.98   Mean   :32.39  
##  3rd Qu.: 25.25   3rd Qu.:40.40   3rd Qu.:38.00   3rd Qu.:39.83  
##  Max.   : 30.63   Max.   :54.60   Max.   :50.60   Max.   :50.60  
##       BCG             BCGf               Tiempo      
##  Min.   :0.0000   Length:139         Min.   :  0.00  
##  1st Qu.:1.0000   Class :character   1st Qu.: 57.00  
##  Median :1.0000   Mode  :character   Median : 65.00  
##  Mean   :0.9496                      Mean   : 63.15  
##  3rd Qu.:1.0000                      3rd Qu.: 75.00  
##  Max.   :1.0000                      Max.   :136.00

Características y relaciones entre variables

Categorización de features

Conociendo que significa cada variable, entendiendo del tipo que son y consoderando que buscamos construir el mejor modelo de regresión que nos permita explicar las muertes debido a causa del COVID-19, podríamos categorizar las mismas en los siguientes grupos:

  • Demográficas: son todas las variables relacionadas a la temática poblacional. Incluimos en esta: Hombres80, Mujeres80, Pobla80, Pobla65, PoblaMid, PoblaData, PoblaDens, Mujeres, Urbano

  • Salud: consideramos las features que hablan sobre enfermedades o topicos de salud ExpectVida, NeontlMort, DisMort, Lesion, EnfNoTrans,Tuberculosis,Diabetes, ImmunSaramp, HipTen.H, HipTen.M, BCG, BCGf

  • Sistema de salud: son las variables relacionadas al tipo de sistema de salud, podríamos considerarlo dentro de salud también Medicos,Camas

  • Económicas: factores del tipo económico PBI

  • Ambientales: consideramos temas de ambiente como la tempratura o el clima TempMarzo

  • Geolocalizacion geoid, CntrName

  • Covid muertes l10muertes.permil, muertes.permil

Considerando que el covid es una enfermedad del tipo respiratoria, altamente contagiosa, que se transmite por aire, afecta con mayor severidad a aquellas personas que tienen una edad superior a 65 años y que si el paciente presenta una patología previa como diabetes, problemas cardíacos, respiratorios u obesidad puede necesitar tratemiento en hospitales. Es interesante estudiar lo siguiente:

  • ¿Los paises de mayor densidad poblacional tienen un porcentaje alto de población mayor a 65 años?.

  • Entender que porcentaje de la población presenta una patología previa, con los datos que tenemos es complejo delimitar este valor y sacar un promedio no sería correcto, por lo que decidimos observar la relación por separado de patología como diabetes e hipertension con las muertes

  • Clasificar a los países por nivel economico utilizando el PBI, que en este caso representa el PBI per capita, y entender si hay alguna relacion con las muertes, si aquellos países con menor poder económico presentan mayor cantidad

  • Ver la relacion entre las camas de hospital y la población de 65 años

  • Considerar la relacion de medicos y camas de hospitales

Análisis gráfico

Relacion entre algunas variables

Países muertes vs poblacion 65

ggplot(covid, aes(x = muertes.permil, y = Pobla65, color = Pobla65)) +
  geom_point()

ggplot(covid, aes(x = muertes.permil, y = PoblaMid, color = PoblaMid)) +
  geom_point()

Menor porcentaje de poblacion mayor a 65 años corresponden menor muertes por mil, mientras que a mayores porcentajes tenemos algunos casos con más muertes. Si lo comparamos vs el grafico que muestra las muertes permil y el porcentaje de población entre 15 y 64 años se puede ver una diferencia en que la mayoría de los puntos estan concentrados en forma vertical cerca del 0 y 50

Países mayor densidad vs poblacion 65

ggplot(covid, aes(x = Pobla65, y = PoblaDens, color = PoblaDens)) +
  geom_point()

Como tenemos un valor fuera del rango no podemos concluir nada.

Muertes vs patologías

ggplot(covid, aes(x = muertes.permil, y = Diabetes,
                 colour = Diabetes)) +
  geom_point(show.legend = FALSE) +
  scale_color_gradient(low = "#67c9ff", high = "#f2bbfc")

ggplot(covid, aes(x = muertes.permil, y = HipTen.H,
                 colour = HipTen.H)) +
  geom_point(show.legend = FALSE) +
  scale_color_gradient(low = "#67c9ff", high = "#f2bbfc")

ggplot(covid, aes(x = muertes.permil, y = HipTen.M,
                 colour = HipTen.M)) +
  geom_point(show.legend = FALSE) +
  scale_color_gradient(low = "#67c9ff", high = "#f2bbfc")

ggplot(covid, aes(x = muertes.permil, y = BCG,
                 colour = BCG)) +
  geom_point(show.legend = FALSE) +
  scale_color_gradient(low = "#67c9ff", high = "#f2bbfc")

No se puede ver que las patologías previas o la inmunización tengan alguna relación directa con las muertes, si se puede ver que en algunos casos para la diabetes e hipertension en poblaciones con porcentajes medios o elevados hay mayor cantidad de muertes. Puede ser que para ese entonces no se haya considerado mucho este factor porque hoy en día se saben que implican mayor riesgo

PBI vs muertes

El producto bruto interno es un indicador economico que tiene en cuenta diversos factores. Si a este lo dividimos por la población del país obtenemos el PBI per capita, o PPP, valor que representa esta variable. Podríamos asumir que aquellos países que tengan un PBI per capita bajo o medio enfrentararn mayor dificultades para implememtar una política de inumización dado que les costará más comprar vacuanas o también debido a que es probable que la gran mayoría de su población esten en condiciones económicas severas no puedan realizar aislamientos, no tengan la educación ni los recursos para intentar no contagiarse de covid, lo que puede llevar a mayor cantidad de casos en países con sistemas de salud escasos provocando mayor cantidad de muertes.

Vamos a generar este indicador y clasificaremos a los países en 4 categorías.

- Ingreso Bajo PBI per capita <1.036

- Ingreso medio Bajo PBI per capita 1.036 - 4.045

- Ingreso medio Alto PBI per capita 4.046 - 12.535

- Ingreso Alto PBI per capita >12.535

ggplot(covid, aes(x = muertes.permil, y = PBI)) +
  geom_point(aes(colour = PBI > 4.045 & PBI < 12.535),
             show.legend = FALSE) +
  geom_hline(yintercept = 4.045, linetype = "dashed") + 
  geom_hline(yintercept = 12.535, linetype = "dashed")+
  xlim(0, 50)+
  ylim(0,60)
## Warning: Removed 34 rows containing missing values (geom_point).

No pareciera que aquellos países con PBI per capita menor tuvieran mayor cantidad de muertes.

Poblacion 65 vs Camas de hospitales

ggplot(covid, aes(x = Pobla65, y = Camas, color = Camas)) +
  geom_point()

Camas vs Medicos

ggplot(covid, aes(x = Camas, y = Medicos,
                 colour = Medicos)) +
  geom_point(show.legend = FALSE) +
  scale_color_gradient(low = "#67c9ff", high = "#f2bbfc")

Comportamiento general

Dado que tenemos varias variables en nuestro dataset para realizar un análisis gráfico representativo seleccionaremos algunas de estas variables para observar su comportamiento.

covid_datos_1 = data.frame(PoblaDens, Pobla80,Urbano, l10muertes.permil)

covid_datos_2 = data.frame(Tuberculosis,Camas, TempMarzo,l10muertes.permil)

covid_datos_3 = data.frame(l10muertes.permil, PBI,muertes.permil,BCG)

covid_datos_1
##       PoblaDens   Pobla80  Urbano l10muertes.permil
## 1    0.56937760 0.2771541  25.495        0.85166698
## 2    1.04612263 2.7410163  60.319        1.09735434
## 3    0.17730075 1.2709846  72.629        1.19736588
## 4    0.24713052 0.2723907  65.514        0.05301271
## 5    0.16258510 2.6111754  91.870        1.08768715
## 6    1.03680225 3.1239347  63.149        1.53403057
## 7    0.03249129 4.0436339  86.012        0.70937665
## 8    1.07206927 5.2369767  58.297        1.86867832
## 9    1.20265320 1.3597424  55.680        0.80830014
## 10  12.39579312 0.9964088  36.632        0.64062316
## 11   0.46728800 3.8045048  78.595        1.37219412
## 12   3.77214927 5.6948380  98.001        2.91424609
## 13   0.16793994 1.0395255  45.724        0.79385771
## 14   1.01853920 0.4365651  47.312        0.10078712
## 15   0.19777528 1.2095158  40.895        0.00000000
## 16   0.10480146 1.6248620  69.425        1.40930364
## 17   0.64920488 3.3861895  48.245        1.66678153
## 18   0.03977425 0.4939442  69.446        0.15945618
## 19   0.25061716 1.8060280  86.569        2.09062497
## 20   0.81396964 0.7583938  77.629        0.75300190
## 21   0.64703537 4.7222857  75.008        1.29960529
## 22   0.72191283 0.2453268  29.358        0.56624131
## 23   4.35178271 0.2917947  13.032        0.03722023
## 24   0.92056413 0.5703887  23.388        0.00000000
## 25   0.53343989 0.3083562  56.374        0.90413575
## 26   0.04075308 4.3071336  81.411        2.26374838
## 27   0.07490412 0.3528256  41.364        0.08432564
## 28   0.44748702 1.7597254  80.778        1.23486238
## 29   4.47244478 0.3889863  28.965        0.53185125
## 30   0.15356846 0.2877278  66.916        0.66491803
## 31   0.73077198 5.5638202  56.947        1.40989932
## 32   1.37602888 4.0918014  73.792        1.48902061
## 33   1.38067302 4.5174188  87.874        1.99324531
## 34   0.41368421 0.6099089  77.777        1.29603122
## 35   2.19978576 1.5873319  81.074        1.65899033
## 36   0.68788682 1.5376442  63.821        2.28487232
## 37   0.98873469 0.7910340  42.704        0.96804820
## 38   3.09881467 1.8419292  72.023        0.84966889
## 39   0.46665740 0.2886712  72.143        1.00721356
## 40   0.30386105 5.5491132  68.880        1.70728513
## 41   1.09224559 0.5012502  20.763        0.02322475
## 42   0.48357033 0.5795601  56.248        0.00000000
## 43   0.18156856 5.4057724  85.382        1.76134842
## 44   1.22338396 6.1032258  80.444        2.63132934
## 45   0.08224764 0.5335561  89.370        0.88115819
## 46   2.25306522 0.2595557  61.270        0.15793307
## 47   0.65275202 3.6827447  58.632        0.62493108
## 48   2.37370970 6.6270801  77.312        2.01040779
## 49   1.30821429 0.3266130  56.060        0.33086008
## 50   0.83224732 7.2679122  79.058        1.23366924
## 51   1.60953779 1.0548473  51.054        0.69394933
## 52   0.50522212 0.3195615  36.140        0.43000974
## 53   0.03957348 1.2722362  26.606        1.17956888
## 54   4.03598549 0.8276328  55.278        0.60817085
## 55   0.85687032 1.0293956  57.096        1.32704478
## 56   1.07906606 4.2713096  71.351        1.72513328
## 57   0.12291734 0.3130915  23.059        0.71053771
## 58   0.25189446 2.7008155  87.564        1.66184336
## 59   1.48348833 1.7877871  59.152        0.63650295
## 60   4.54938073 0.9448065  34.030        0.63846947
## 61   1.47752190 0.8561815  55.325        0.81312576
## 62   0.50222420 1.1233084  74.898        1.97066822
## 63   0.88530570 0.4831038  70.473        0.74455172
## 64   0.70452983 3.0188525  63.170        2.52768884
## 65   4.10526802 3.0461867  92.418        1.51362517
## 66   2.05450748 7.1734357  70.438        2.73899149
## 67   2.70993075 2.0456797  55.674        0.60923050
## 68   3.47073458 8.3613475  91.616        0.89103814
## 69   1.12142498 0.6023762  90.979        0.27966158
## 70   0.06769826 1.4932044  57.428        0.48064752
## 71   0.90299417 0.2699139  27.030        0.31600904
## 72   2.32172222 0.2327419 100.000        1.63646809
## 73   0.30983307 5.5652960  68.142        1.11188355
## 74   6.69494135 1.4828557  88.593        0.68089875
## 75   0.69437813 0.8209008  28.153        0.00000000
## 76   0.50030907 0.3996440  51.151        0.81973136
## 77   0.03795632 0.7605248  80.102        0.20382969
## 78   0.44531351 5.8370281  67.679        1.39199086
## 79   2.50093827 4.0235270  90.981        2.26007623
## 80   0.45139856 0.4241700  37.191        0.03187466
## 81   1.92440762 0.3087382  16.937        0.08652600
## 82   0.95962821 1.0741699  76.036        0.66721788
## 83   0.15635016 0.2683715  42.356        0.66924316
## 84   0.04272164 0.4204185  53.672        0.66592067
## 85   6.23301970 2.0227961  40.793        0.94954834
## 86   0.64914626 1.5523039  80.156        1.83964772
## 87   1.23519804 2.2672524  42.629        1.89361039
## 88   0.02040609 0.6283914  68.445        0.00000000
## 89   0.46271004 3.4204855  66.813        1.18924972
## 90   0.80728519 1.1687236  62.453        0.81997625
## 91   0.37508535 0.3481273  35.988        0.01447977
## 92   0.82238615 0.7791075  30.579        0.04599322
## 93   0.02973746 0.6151794  50.032        0.00000000
## 94   1.95939107 0.7516195  19.740        0.05782208
## 95   5.11457910 4.6976392  91.490        2.53367393
## 96   0.18554176 3.7311137  86.538        0.74060910
## 97   0.53727048 1.1025688  58.522        0.80708405
## 98   0.17717651 0.2340882  16.425        0.58564967
## 99   0.14554920 4.2231531  82.248        1.65533067
## 100  0.15604145 0.4596337  84.539        0.94784219
## 101  2.75289319 0.6545620  36.666        0.84119511
## 102  0.56186077 1.9205337  67.709        1.88318015
## 103  0.17508359 1.2135437  61.585        0.41184731
## 104  0.24991606 1.6809015  77.907        2.09868025
## 105  3.57688305 0.7893793  46.907        0.97663292
## 106  1.24035886 4.4631164  60.058        1.44821011
## 107  1.12239454 6.3408699  65.211        2.12347272
## 108  2.39593196 0.1847213  99.135        1.07132447
## 109  0.84639847 4.7084438  53.998        1.80343313
## 110  4.98659870 0.3624706  17.211        0.00000000
## 111  2.82589744 2.4482249  52.198        0.00000000
## 112  0.15676654 0.5038453  83.844        1.13389947
## 113  0.82347478 0.3825330  47.192        0.53906244
## 114  0.79831740 4.0191136  56.092        1.54868038
## 115  1.05987171 0.3522140  42.055        0.83772947
## 116 79.52998418 2.1766595 100.000        0.70577580
## 117  1.13290578 3.2223629  53.726        0.78819906
## 118  1.02639860 5.3152643  54.541        1.72227622
## 119  0.47630120 0.7184723  66.355        1.02339821
## 120  5.29652104 3.2120425  81.459        0.79306494
## 121  0.93529058 6.1672057  80.321        2.76446761
## 122  3.45558922 1.6065781  18.476        0.16478915
## 123  0.25001043 5.1726032  87.431        2.61847597
## 124  2.15521378 5.1493834  73.797        2.28867018
## 125  0.65572714 0.5234433  27.134        0.78207686
## 126  1.35897207 2.5315654  49.949        0.26030714
## 127  1.45046773 0.2700138  41.702        0.42289247
## 128  0.74441323 1.7197442  68.945        0.71183934
## 129  1.06960129 1.6774797  75.143        1.73570816
## 130  2.13061734 0.2057545  23.774        0.00000000
## 131  0.77029667 3.9876216  69.352        1.18842716
## 132  1.35609110 0.1307769  86.522        1.43897112
## 133  2.74827392 5.0282090  83.398        2.75158819
## 134  0.35766089 3.8751036  82.256        2.48855760
## 135  0.19708028 4.3947641  95.334        0.86794498
## 136  0.77469205 0.8369038  50.478        0.15375896
## 137  0.53977853 0.3498816  36.642        0.45632576
## 138  0.23341479 0.2529708  43.521        0.14718638
## 139  0.37324591 0.4222739  32.209        0.10620013
covid_datos_2
##     Tuberculosis      Camas TempMarzo l10muertes.permil
## 1          189.0  0.4363636      7.60        0.85166698
## 2           18.0  2.9375000      6.04        1.09735434
## 3           69.0  1.9000000     17.91        1.19736588
## 4          355.0  0.8000000     22.78        0.05301271
## 5           27.0  4.6000000     17.51        1.08768715
## 6           31.0  4.0200000     -0.57        1.53403057
## 7            6.6  3.8783333     25.37        0.70937665
## 8            7.1  7.7000000      1.42        1.86867832
## 9           63.0  6.4666667      4.97        0.80830014
## 10         221.0  0.5750000     25.42        0.64062316
## 11          31.0 11.2000000     -0.69        1.37219412
## 12           9.0  6.5500000      5.23        2.91424609
## 13          30.0  1.1600000     24.45        0.79385771
## 14          56.0  0.5000000     30.14        0.10078712
## 15         149.0  1.7333333      5.68        0.00000000
## 16         108.0  1.1000000     22.07        1.40930364
## 17          25.0  3.3666667      4.01        1.66678153
## 18         275.0  2.0000000     24.30        0.15945618
## 19          45.0  2.3285714     25.49        2.09062497
## 20          68.0  2.7542857     25.92        0.75300190
## 21          22.0  6.4888889      4.70        1.29960529
## 22          48.0  0.6500000     30.63        0.56624131
## 23         111.0  1.1333333     20.43        0.03722023
## 24         302.0  0.7600000     27.93        0.00000000
## 25         186.0  1.4000000     26.27        0.90413575
## 26           5.6  3.0714286    -18.72        2.26374838
## 27         540.0  1.1000000     26.96        0.08432564
## 28          33.0  1.3166667     25.14        1.23486238
## 29          35.0  2.2000000     25.20        0.53185125
## 30         375.0  1.6000000     25.60        0.66491803
## 31           8.4  5.6354545      5.83        1.40989932
## 32           5.4  7.0100000      2.80        1.48902061
## 33           5.4  3.4111111      2.24        1.99324531
## 34         260.0  1.4571429     25.75        1.29603122
## 35          45.0  1.4375000     22.88        1.65899033
## 36          44.0  1.5285714     21.93        2.28487232
## 37          12.0  1.5100000     17.83        0.96804820
## 38          70.0  0.9900000     25.70        0.84966889
## 39         201.0  2.0666667     25.04        1.00721356
## 40          13.0  5.3600000     -2.35        1.70728513
## 41         151.0  1.7500000     23.48        0.02322475
## 42          54.0  2.1475000     25.00        0.00000000
## 43           4.7  5.9600000     -6.09        1.76134842
## 44           8.9  6.9222222      6.37        2.63132934
## 45         525.0  3.2000000     26.14        0.88115819
## 46         174.0  1.0000000     27.32        0.15793307
## 47          80.0  3.1222222      0.72        0.62493108
## 48           7.3  8.2555556      3.87        2.01040779
## 49         148.0  0.9000000     29.52        0.33086008
## 50           4.5  4.6555556      8.19        1.23366924
## 51          26.0  0.6250000     22.94        0.69394933
## 52         176.0  0.3000000     27.63        0.43000974
## 53          83.0  2.1600000     25.65        1.17956888
## 54         176.0  1.0000000     23.47        0.60817085
## 55          37.0  0.7285714     23.38        1.32704478
## 56           6.4  7.3000000      5.44        1.72513328
## 57         142.0  0.4000000     26.55        0.71053771
## 58          18.0  2.1714286     10.87        1.66184336
## 59          61.0  3.4066667      0.49        0.63650295
## 60         199.0  0.8000000     23.45        0.63846947
## 61         316.0  0.9000000     25.79        0.81312576
## 62          14.0  1.4400000     11.33        1.97066822
## 63          42.0  1.3100000     15.12        0.74455172
## 64           7.0  3.9555556      6.00        2.52768884
## 65           4.0  3.5333333     14.96        1.51362517
## 66           7.0  3.6875000      6.52        2.73899149
## 67           2.9  1.7571429     23.44        0.60923050
## 68          14.0 13.7960000      2.52        0.89103814
## 69           5.0  1.7818182     13.08        0.27966158
## 70          68.0  7.4111111     -3.96        0.48064752
## 71         292.0  1.4000000     26.10        0.31600904
## 72          23.0  1.9800000     19.23        1.63646809
## 73          29.0  6.8111111     -1.37        1.11188355
## 74          11.0  3.3800000     10.39        0.68089875
## 75         611.0  1.3000000     15.13        0.00000000
## 76         308.0  0.7500000     26.44        0.81973136
## 77          40.0  3.6700000     17.76        0.20382969
## 78          44.0  7.1666667     -0.47        1.39199086
## 79           8.0  5.3727273      4.70        2.26007623
## 80         233.0  0.2500000     23.99        0.03187466
## 81         181.0  1.2000000     23.25        0.08652600
## 82          92.0  1.8312500     25.19        0.66721788
## 83          53.0  0.3333333     27.34        0.66924316
## 84          93.0  0.4000000     25.29        0.66592067
## 85          13.0  3.2333333     25.48        0.94954834
## 86          23.0  1.5888889     18.44        1.83964772
## 87          86.0  6.1666667      2.93        1.89361039
## 88         428.0  6.3266667     -8.80        0.00000000
## 89          15.0  4.0125000      2.53        1.18924972
## 90          99.0  0.9800000     13.09        0.81997625
## 91         551.0  0.7666667     25.48        0.01447977
## 92         338.0  0.7500000     22.74        0.04599322
## 93         524.0  3.0000000     22.67        0.00000000
## 94         151.0  2.6500000     10.02        0.05782208
## 95           5.3  4.6000000      4.95        2.53367393
## 96           7.3  2.5500000     13.60        0.74060910
## 97          41.0  0.9222222     24.95        0.80708405
## 98          87.0  0.3000000     26.01        0.58564967
## 99           4.1  4.2555556     -5.49        1.65533067
## 100          5.9  1.8600000     23.40        0.94784219
## 101        265.0  0.7200000     16.01        0.84119511
## 102         52.0  2.2888889     25.44        1.88318015
## 103         43.0  1.2857143     25.84        0.41184731
## 104        123.0  1.4875000     19.99        2.09868025
## 105        554.0  0.5833333     25.13        0.97663292
## 106         16.0  6.5222222      2.65        1.44821011
## 107         24.0  3.4222222     11.33        2.12347272
## 108         31.0  1.6400000     21.78        1.07132447
## 109         68.0  6.4555556      3.40        1.80343313
## 110         59.0  1.6000000     19.25        0.00000000
## 111          6.3  3.3000000     25.90        0.00000000
## 112         10.0  2.2500000     20.60        1.13389947
## 113        118.0  0.2000000     28.47        0.53906244
## 114         17.0  5.6125000      4.91        1.54868038
## 115        298.0  0.4000000     27.65        0.83772947
## 116         47.0  2.7457143     28.62        0.70577580
## 117          5.8  6.3400000      2.40        0.78819906
## 118          5.3  4.6555556      3.38        1.72227622
## 119        520.0  2.8000000     21.10        1.02339821
## 120         66.0 10.6080000      3.66        0.79306494
## 121          9.4  3.1888889      8.60        2.76446761
## 122         64.0  3.5500000     27.03        0.16478915
## 123          5.5  2.7666667     -4.98        2.61847597
## 124          6.4  5.1222222      0.09        2.28867018
## 125         84.0  5.2666667     -3.08        0.78207686
## 126        153.0  2.1000000     27.38        0.26030714
## 127         36.0  0.8000000     29.31        0.42289247
## 128         35.0  2.0545455     14.21        0.71183934
## 129         16.0  2.4555556      4.77        1.73570816
## 130        200.0  0.7500000     23.68        0.00000000
## 131         80.0  8.9222222      1.18        1.18842716
## 132          1.0  1.6777778     22.61        1.43897112
## 133          8.0  3.2111111      4.66        2.75158819
## 134          3.0  3.0333333      0.06        2.48855760
## 135         33.0  2.5250000     21.26        0.86794498
## 136         70.0  4.5888889      5.38        0.15375896
## 137         48.0  0.6900000     20.82        0.45632576
## 138        346.0  1.9500000     22.90        0.14718638
## 139        210.0  2.3500000     22.92        0.10620013
covid_datos_3
##     l10muertes.permil         PBI muertes.permil BCG
## 1          0.85166698   1.8351696     6.10668360   1
## 2          1.09735434  11.3351950    11.51279525   1
## 3          1.19736588  14.1967389    14.75309441   1
## 4          0.05301271   6.7205961     0.12982898   1
## 5          1.08768715  20.0684923    11.23734344   1
## 6          1.53403057   8.3491802    33.20035125   1
## 7          0.70937665  45.7525548     4.12125797   1
## 8          1.86867832  48.9687140    72.90576495   1
## 9          0.80830014  17.0906963     5.43132025   1
## 10         0.64062316   3.3061083     3.37142634   1
## 11         1.37219412  18.1721809    22.56102177   1
## 12         2.91424609  45.2631622   819.81651659   0
## 13         0.79385771   8.0937796     5.22096426   1
## 14         0.10078712   2.0675705     0.26120918   1
## 15         0.00000000   8.3417246     0.00000000   1
## 16         1.40930364   6.5317860    24.66277617   1
## 17         1.66678153  11.6971771    45.42816649   1
## 18         0.15945618  16.1336867     0.44363092   1
## 19         2.09062497  15.5847506   122.20404597   1
## 20         0.75300190  80.8004129     4.66241765   1
## 21         1.29960529  17.9465777    18.93449746   1
## 22         0.56624131   1.6709928     2.68333575   1
## 23         0.03722023   0.7568378     0.08948243   1
## 24         0.00000000   3.3333517     0.00000000   1
## 25         0.90413575   3.2930885     7.01928682   1
## 26         2.26374838  44.2264907   182.54745910   0
## 27         0.08432564   0.8532664     0.21429902   1
## 28         1.23486238  13.2116325    16.17364085   1
## 29         0.53185125   2.6420144     2.40291618   1
## 30         0.66491803   5.6636489     3.62293762   1
## 31         1.40989932  22.9922117    24.69799971   1
## 32         1.48902061  32.7718057    29.83334267   1
## 33         1.99324531  48.5249925    97.45670766   1
## 34         1.29603122   2.7442687    18.77111751   1
## 35         1.65899033  13.9050879    44.60267625   1
## 36         2.28487232  10.8759023   191.69583028   1
## 37         0.96804820  10.8110341     8.29069493   1
## 38         0.84966889   7.2348467     6.07406245   1
## 39         1.00721356  30.5908474     9.16748537   1
## 40         1.70728513  28.8340001    49.96653756   1
## 41         0.02322475   1.5126247     0.05493270   1
## 42         0.00000000   8.8754441     0.00000000   1
## 43         1.76134842  42.8552430    56.72293654   1
## 44         2.63132934  40.3515680   426.88724438   1
## 45         0.88115819  17.0778163     6.60603272   1
## 46         0.15793307   2.4051919     0.43857687   1
## 47         0.62493108   9.5834608     3.21629590   1
## 48         2.01040779  46.5762068   101.42542822   1
## 49         0.33086008   3.9219764     1.14220031   1
## 50         1.23366924  27.2065489    16.12652442   1
## 51         0.69394933   7.5159291     3.94253020   1
## 52         0.43000974   2.0022876     1.69159514   1
## 53         1.17956888   7.1987245    14.12059502   1
## 54         0.60817085   1.7058976     3.05668093   1
## 55         1.32704478   4.4336960    20.23463414   1
## 56         1.72513328  25.7573736    52.10473974   1
## 57         0.71053771   2.0073216     4.13496767   1
## 58         1.66184336  22.2523960    44.90324179   1
## 59         0.63650295  13.5313781     3.33015014   1
## 60         0.63846947   5.8366566     3.34980183   1
## 61         0.81312576  10.5772045     5.50317977   1
## 62         1.97066822  18.4501839    92.46913357   1
## 63         0.74455172  15.8895142     4.55330752   1
## 64         2.52768884  59.3055780   336.04573683   1
## 65         1.51362517  34.5710854    31.63060852   1
## 66         2.73899149  37.7630816   547.26622303   0
## 67         0.60923050   8.5085469     3.06659102   1
## 68         0.89103814  39.1530060     6.78104879   1
## 69         0.27966158   9.2014965     0.90397650   1
## 70         0.48064752  24.1030338     2.02445775   1
## 71         0.31600904   2.8839337     1.07018445   1
## 72         1.63646809  75.8307296    42.29802512   1
## 73         1.11188355  23.8146826    11.93848875   1
## 74         0.68089875  13.2450688     3.79621619   0
## 75         0.00000000   2.9043623     0.00000000   1
## 76         0.81973136   1.2563111     5.60284890   1
## 77         0.20382969  19.3396159     0.59893088   1
## 78         1.39199086  27.8087523    23.65987425   1
## 79         2.26007623 100.2191161   181.00202722   1
## 80         0.03187466   1.7044431     0.07615460   1
## 81         0.08652600   1.1836484     0.22046688   1
## 82         0.66721788  25.8714034     3.64748370   1
## 83         0.66924316   2.0131236     3.66920733   1
## 84         0.66592067   3.8172107     3.63362273   1
## 85         0.94954834  19.3987280     7.90324531   1
## 86         1.83964772  17.8555895    68.12700147   1
## 87         1.89361039   5.8933274    77.27271317   1
## 88         0.00000000  11.1277975     0.00000000   1
## 89         1.18924972  16.2734892    14.46143216   1
## 90         0.81997625   7.4795258     5.60657321   1
## 91         0.01447977   1.2616857     0.03390295   1
## 92         0.04599322   5.0214146     0.11171438   1
## 93         0.00000000  10.2230967     0.00000000   1
## 94         0.05782208   2.4752041     0.14241022   1
## 95         2.53367393  49.9843155   340.72277916   0
## 96         0.74060910  36.4995043     4.50312148   1
## 97         0.80708405   4.8837325     5.41333688   1
## 98         0.58564967   0.9278714     2.85167528   1
## 99         1.65533067  62.6503184    44.22001168   1
## 100        0.94784219  42.4790524     7.86833705   1
## 101        0.84119511   4.6501098     5.93737399   1
## 102        1.88318015  20.8839055    75.41526879   1
## 103        0.41184731  11.4029323     1.58135246   1
## 104        2.09868025  12.3515166   124.51055442   1
## 105        0.97663292   7.0082665     8.47617167   1
## 106        1.44821011  25.9904310    27.06791213   1
## 107        2.12347272  29.3390041   131.88400976   1
## 108        1.07132447 123.2139364    10.78486107   1
## 109        1.80343313  21.6182719    62.59648794   1
## 110        0.00000000   1.7889788     0.00000000   1
## 111        0.00000000  10.9317631     0.00000000   1
## 112        1.13389947  51.5878305    12.61129580   1
## 113        0.53906244   3.1310271     2.45989116   1
## 114        1.54868038  14.9080484    34.37369129   1
## 115        0.83772947   1.4959394     5.88223453   1
## 116        0.70577580  86.0684237     4.07897173   1
## 117        0.78819906  29.0915206     5.14043390   1
## 118        1.72227622  31.7402860    51.75652955   1
## 119        1.02339821  12.8666891     9.55354121   1
## 120        0.79306494  34.6370853     5.20961879   1
## 121        2.76446761  34.5888453   580.39007101   1
## 122        0.16478915  11.1185327     0.46146747   1
## 123        2.61847597  47.6285919   414.40906201   0
## 124        2.28867018  61.3146089   193.38832670   1
## 125        0.78207686   2.7293337     5.05448015   1
## 126        0.26030714  15.8571484     0.82098822   1
## 127        0.42289247   1.4956789     1.64784448   1
## 128        0.71183934  11.2660561     4.15038075   1
## 129        1.73570816  23.5212137    53.41368734   1
## 130        0.00000000   1.8103287     0.00000000   1
## 131        1.18842716   8.4417522    14.43217590   1
## 132        1.43897112  65.5180899    26.47711407   1
## 133        2.75158819  41.1611271   563.40154117   1
## 134        2.48855760  55.0581658   307.00488362   0
## 135        0.86794498  20.4797528     6.37810755   1
## 136        0.15375896   6.8361061     0.42481657   1
## 137        0.45632576   3.5314239     1.85973480   1
## 138        0.14718638   3.8025013     0.40341585   1
## 139        0.10620013   2.5606953     0.27702715   1

Correlacion entre variables, distribución y scaterplots

ggpairs(covid_datos_1,title="Covid")

ggpairs(covid_datos_2,title="Covid")

ggpairs(covid_datos_3,title="Covid")

Análisis

  • La variable l10muertes.permil tiene las siguientes correlaciónes

    • Positiva:

      • pob80 0.688 relacion fuerte

      • urbano 0.57 relacion fuerte

      • camas 0.3 relacion media

      • muertes.permil 0.7 relación fuerte

      • PBI 0.5 relación fuerte

    • Negativa:

      • Tuberculosis 0.4 relacion fuerte

      • Tempmarzo 0.5 relacion fuerte

      • BCG

  • De los scaterplot podemos concluir que tenemos una nube de puntos que podría explicarse con una regresión lineal simple para la población de 80 años y la zona urbana, como estas variables vs el logaritmo de las muertes por millón de habitante. También es para el caso de la variable PBI vs esta última. En el resto se observan nubes de puntos concentradas en algún cuadrante

  • Otras correlaciones interesantes son:

    • Tuberculosis y la temperatura de marzo, lo cual hace sentido dado que la posibilidad de padecer dicha enfermedad incrementa con temperaturas frías o disminuye para climas más tempaldos

    • La temperatura de marzo y las camas también presentan una correlación fuerte y negativa

    • EL PBI tiene una correlación negativa y debil comparda con la política de inmunización

Histogramas

Utilizamos histogramas para ver la distribución de las variables que nos resultan de mayor interes, en este caso seleccionamos PBI, Temperatura, Camas, urbano ,población de 80 años y el logaritmo de las muertes.

#PoblaDens, Pobla80,Urbano, l10muertes.permil,Tuberculosis,Camas, TempMarzo,PBI,muertes.permil,BCG

hp <- covid %>%
  ggplot( aes(x=Pobla80)) +
    geom_histogram( binwidth=3, fill="#69b3a2", color="#e9ecef", alpha=0.9) +
    ggtitle("Distribucion de poblacion 80") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=8)
    )


hu <- covid %>%
  ggplot( aes(x=Urbano)) +
    geom_histogram( binwidth=3, fill="#9a76db", color="#e9ecef", alpha=0.9) +
    ggtitle("Distribucion de urbano") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=8)
    )


hpb <- covid %>%
  ggplot( aes(x=PBI)) +
    geom_histogram( binwidth=3, fill="#60bd88", color="#e9ecef", alpha=0.9) +
    ggtitle("Distribucion del PBI") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=8)
    )

ht <- covid %>%
  ggplot( aes(x=TempMarzo)) +
    geom_histogram( binwidth=3, fill="#609ebd", color="#e9ecef", alpha=0.9) +
    ggtitle("Distribucion de la Temperatura en Marzo") +
    theme_ipsum() +
    theme(
      plot.title = element_text(size=8)
    )

ggarrange(hp,hu,hpb,ht,
          nrow = 1,
          ncol = 2
          )
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database
## $`1`

## 
## $`2`

## 
## attr(,"class")
## [1] "list"      "ggarrange"

Boxplot

 bp <- covid %>%
  ggplot(aes(y=Pobla80)) +
    geom_boxplot(binwidth=0.5,fill="#69b3a2", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Poblacion 80") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
  bu <- covid %>%
  ggplot(aes(y=Urbano)) +
    geom_boxplot(binwidth=0.5,fill="#db769d", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Urbano") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
   bpbi <- covid %>%
  ggplot(aes(y=PBI)) +
    geom_boxplot(binwidth=0.5,fill="#9a76db", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("PBI") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
  bt <- covid %>%
  ggplot(aes(y=TempMarzo)) +
    geom_boxplot(binwidth=0.5,fill="#bd6099", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Temperatura") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
    btu <- covid %>%
  ggplot(aes(y=Tuberculosis)) +
    geom_boxplot(binwidth=0.5,fill="#99bd60", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Tuberculosis") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
      bca <- covid %>%
  ggplot(aes(y=Camas)) +
    geom_boxplot(binwidth=0.5,fill="#bd9e60", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Camas") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
   blm <- covid %>%
  ggplot(aes(y=l10muertes.permil)) +
    geom_boxplot(binwidth=0.5,fill="#bd7660", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Logaritmo Muertes") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
    bpm <- covid %>%
  ggplot(aes(y=muertes.permil)) +
    geom_boxplot(binwidth=0.5,fill="#79ccd9", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Muertes por millón") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
 ggarrange( bp,bu,bpbi,bt,btu,bca,blm,bpm,
            nrow = 1,
            ncol = 2)
## $`1`

## 
## $`2`

## 
## $`3`

## 
## $`4`

## 
## attr(,"class")
## [1] "list"      "ggarrange"

Las variables tuberculosis, camas y PBI presentas valores atípicos de magnitud alta y positiva

Análisis boxplot para l10muertes.permil y muertes.permil en relacion a BCGf

  blm <- covid %>%
  ggplot(aes(y=l10muertes.permil)) +
    facet_wrap(~BCGf) +
    geom_boxplot(binwidth=0.5,fill="#bd7660", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Logaritmo Muertes") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
    bpm <- covid %>%
  ggplot(aes(y=muertes.permil)) +
    facet_wrap(~BCGf) +
    geom_boxplot(binwidth=0.5,fill="#79ccd9", color="#141414") +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=8)
    ) +
    ggtitle("Muertes por millón") +
    xlab("")
## Warning: Ignoring unknown parameters: binwidth
 ggarrange( blm,bpm,
            nrow = 1,
            ncol = 1)
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## 'Arial Narrow' not found in PostScript font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family 'Arial Narrow' not found in PostScript font database
## $`1`

## 
## $`2`

## 
## attr(,"class")
## [1] "list"      "ggarrange"

Análisis

Los efectos de la política de inmunización adoptada pueden verse reflejados de una manera más sencilla, dado que los correspondientes a no toman valores más altos relacionado a la muertes y menores para los de inmunización total. Lo cuál ayuda a la idea que en ese momento se tenía que esta inmunidad colaboraba a disminuir los efectos del covid

Por otro lado encambio para el caso de las muertes por millón no es tan sencillo diferenciar los efectos de la política implementada y esta variable se ve más influenciada por valores atípicos.

Correlación General

Vamos a realizar una matriz de correlación general considerando todas las variables

# Linda forma de visualizar la matriz de correlacion
ggcorr(covid, method = c("everything", "pearson")) 
## Warning in ggcorr(covid, method = c("everything", "pearson")): data in column(s)
## 'geoId', 'CntrName', 'BCGf' are not numeric and were ignored

Claramente tener tantas variables contempladas en el gráfico no colabora pero es una buena forma de tener una idea rápida.

Modelo de regresión

Vamos a considerar un número de variables reducidas para proponer un modelo de regresión. Como variables regresoras tenemos PoblaDens, Pobla80,Urbano, Tuberculosis, Camas, TempMarzo y PBI utilizaremos como variable de respuesta a l10muertes.permil

*Aclaración todas las variables son del tipo numérico

Generamos un dataframe con las variables seleccionadas unicamente

df_covid = data.frame(PoblaDens, Pobla80,Urbano, l10muertes.permil,Tuberculosis,Camas, TempMarzo,PBI)
df_covid
##       PoblaDens   Pobla80  Urbano l10muertes.permil Tuberculosis      Camas
## 1    0.56937760 0.2771541  25.495        0.85166698        189.0  0.4363636
## 2    1.04612263 2.7410163  60.319        1.09735434         18.0  2.9375000
## 3    0.17730075 1.2709846  72.629        1.19736588         69.0  1.9000000
## 4    0.24713052 0.2723907  65.514        0.05301271        355.0  0.8000000
## 5    0.16258510 2.6111754  91.870        1.08768715         27.0  4.6000000
## 6    1.03680225 3.1239347  63.149        1.53403057         31.0  4.0200000
## 7    0.03249129 4.0436339  86.012        0.70937665          6.6  3.8783333
## 8    1.07206927 5.2369767  58.297        1.86867832          7.1  7.7000000
## 9    1.20265320 1.3597424  55.680        0.80830014         63.0  6.4666667
## 10  12.39579312 0.9964088  36.632        0.64062316        221.0  0.5750000
## 11   0.46728800 3.8045048  78.595        1.37219412         31.0 11.2000000
## 12   3.77214927 5.6948380  98.001        2.91424609          9.0  6.5500000
## 13   0.16793994 1.0395255  45.724        0.79385771         30.0  1.1600000
## 14   1.01853920 0.4365651  47.312        0.10078712         56.0  0.5000000
## 15   0.19777528 1.2095158  40.895        0.00000000        149.0  1.7333333
## 16   0.10480146 1.6248620  69.425        1.40930364        108.0  1.1000000
## 17   0.64920488 3.3861895  48.245        1.66678153         25.0  3.3666667
## 18   0.03977425 0.4939442  69.446        0.15945618        275.0  2.0000000
## 19   0.25061716 1.8060280  86.569        2.09062497         45.0  2.3285714
## 20   0.81396964 0.7583938  77.629        0.75300190         68.0  2.7542857
## 21   0.64703537 4.7222857  75.008        1.29960529         22.0  6.4888889
## 22   0.72191283 0.2453268  29.358        0.56624131         48.0  0.6500000
## 23   4.35178271 0.2917947  13.032        0.03722023        111.0  1.1333333
## 24   0.92056413 0.5703887  23.388        0.00000000        302.0  0.7600000
## 25   0.53343989 0.3083562  56.374        0.90413575        186.0  1.4000000
## 26   0.04075308 4.3071336  81.411        2.26374838          5.6  3.0714286
## 27   0.07490412 0.3528256  41.364        0.08432564        540.0  1.1000000
## 28   0.44748702 1.7597254  80.778        1.23486238         33.0  1.3166667
## 29   4.47244478 0.3889863  28.965        0.53185125         35.0  2.2000000
## 30   0.15356846 0.2877278  66.916        0.66491803        375.0  1.6000000
## 31   0.73077198 5.5638202  56.947        1.40989932          8.4  5.6354545
## 32   1.37602888 4.0918014  73.792        1.48902061          5.4  7.0100000
## 33   1.38067302 4.5174188  87.874        1.99324531          5.4  3.4111111
## 34   0.41368421 0.6099089  77.777        1.29603122        260.0  1.4571429
## 35   2.19978576 1.5873319  81.074        1.65899033         45.0  1.4375000
## 36   0.68788682 1.5376442  63.821        2.28487232         44.0  1.5285714
## 37   0.98873469 0.7910340  42.704        0.96804820         12.0  1.5100000
## 38   3.09881467 1.8419292  72.023        0.84966889         70.0  0.9900000
## 39   0.46665740 0.2886712  72.143        1.00721356        201.0  2.0666667
## 40   0.30386105 5.5491132  68.880        1.70728513         13.0  5.3600000
## 41   1.09224559 0.5012502  20.763        0.02322475        151.0  1.7500000
## 42   0.48357033 0.5795601  56.248        0.00000000         54.0  2.1475000
## 43   0.18156856 5.4057724  85.382        1.76134842          4.7  5.9600000
## 44   1.22338396 6.1032258  80.444        2.63132934          8.9  6.9222222
## 45   0.08224764 0.5335561  89.370        0.88115819        525.0  3.2000000
## 46   2.25306522 0.2595557  61.270        0.15793307        174.0  1.0000000
## 47   0.65275202 3.6827447  58.632        0.62493108         80.0  3.1222222
## 48   2.37370970 6.6270801  77.312        2.01040779          7.3  8.2555556
## 49   1.30821429 0.3266130  56.060        0.33086008        148.0  0.9000000
## 50   0.83224732 7.2679122  79.058        1.23366924          4.5  4.6555556
## 51   1.60953779 1.0548473  51.054        0.69394933         26.0  0.6250000
## 52   0.50522212 0.3195615  36.140        0.43000974        176.0  0.3000000
## 53   0.03957348 1.2722362  26.606        1.17956888         83.0  2.1600000
## 54   4.03598549 0.8276328  55.278        0.60817085        176.0  1.0000000
## 55   0.85687032 1.0293956  57.096        1.32704478         37.0  0.7285714
## 56   1.07906606 4.2713096  71.351        1.72513328          6.4  7.3000000
## 57   0.12291734 0.3130915  23.059        0.71053771        142.0  0.4000000
## 58   0.25189446 2.7008155  87.564        1.66184336         18.0  2.1714286
## 59   1.48348833 1.7877871  59.152        0.63650295         61.0  3.4066667
## 60   4.54938073 0.9448065  34.030        0.63846947        199.0  0.8000000
## 61   1.47752190 0.8561815  55.325        0.81312576        316.0  0.9000000
## 62   0.50222420 1.1233084  74.898        1.97066822         14.0  1.4400000
## 63   0.88530570 0.4831038  70.473        0.74455172         42.0  1.3100000
## 64   0.70452983 3.0188525  63.170        2.52768884          7.0  3.9555556
## 65   4.10526802 3.0461867  92.418        1.51362517          4.0  3.5333333
## 66   2.05450748 7.1734357  70.438        2.73899149          7.0  3.6875000
## 67   2.70993075 2.0456797  55.674        0.60923050          2.9  1.7571429
## 68   3.47073458 8.3613475  91.616        0.89103814         14.0 13.7960000
## 69   1.12142498 0.6023762  90.979        0.27966158          5.0  1.7818182
## 70   0.06769826 1.4932044  57.428        0.48064752         68.0  7.4111111
## 71   0.90299417 0.2699139  27.030        0.31600904        292.0  1.4000000
## 72   2.32172222 0.2327419 100.000        1.63646809         23.0  1.9800000
## 73   0.30983307 5.5652960  68.142        1.11188355         29.0  6.8111111
## 74   6.69494135 1.4828557  88.593        0.68089875         11.0  3.3800000
## 75   0.69437813 0.8209008  28.153        0.00000000        611.0  1.3000000
## 76   0.50030907 0.3996440  51.151        0.81973136        308.0  0.7500000
## 77   0.03795632 0.7605248  80.102        0.20382969         40.0  3.6700000
## 78   0.44531351 5.8370281  67.679        1.39199086         44.0  7.1666667
## 79   2.50093827 4.0235270  90.981        2.26007623          8.0  5.3727273
## 80   0.45139856 0.4241700  37.191        0.03187466        233.0  0.2500000
## 81   1.92440762 0.3087382  16.937        0.08652600        181.0  1.2000000
## 82   0.95962821 1.0741699  76.036        0.66721788         92.0  1.8312500
## 83   0.15635016 0.2683715  42.356        0.66924316         53.0  0.3333333
## 84   0.04272164 0.4204185  53.672        0.66592067         93.0  0.4000000
## 85   6.23301970 2.0227961  40.793        0.94954834         13.0  3.2333333
## 86   0.64914626 1.5523039  80.156        1.83964772         23.0  1.5888889
## 87   1.23519804 2.2672524  42.629        1.89361039         86.0  6.1666667
## 88   0.02040609 0.6283914  68.445        0.00000000        428.0  6.3266667
## 89   0.46271004 3.4204855  66.813        1.18924972         15.0  4.0125000
## 90   0.80728519 1.1687236  62.453        0.81997625         99.0  0.9800000
## 91   0.37508535 0.3481273  35.988        0.01447977        551.0  0.7666667
## 92   0.82238615 0.7791075  30.579        0.04599322        338.0  0.7500000
## 93   0.02973746 0.6151794  50.032        0.00000000        524.0  3.0000000
## 94   1.95939107 0.7516195  19.740        0.05782208        151.0  2.6500000
## 95   5.11457910 4.6976392  91.490        2.53367393          5.3  4.6000000
## 96   0.18554176 3.7311137  86.538        0.74060910          7.3  2.5500000
## 97   0.53727048 1.1025688  58.522        0.80708405         41.0  0.9222222
## 98   0.17717651 0.2340882  16.425        0.58564967         87.0  0.3000000
## 99   0.14554920 4.2231531  82.248        1.65533067          4.1  4.2555556
## 100  0.15604145 0.4596337  84.539        0.94784219          5.9  1.8600000
## 101  2.75289319 0.6545620  36.666        0.84119511        265.0  0.7200000
## 102  0.56186077 1.9205337  67.709        1.88318015         52.0  2.2888889
## 103  0.17508359 1.2135437  61.585        0.41184731         43.0  1.2857143
## 104  0.24991606 1.6809015  77.907        2.09868025        123.0  1.4875000
## 105  3.57688305 0.7893793  46.907        0.97663292        554.0  0.5833333
## 106  1.24035886 4.4631164  60.058        1.44821011         16.0  6.5222222
## 107  1.12239454 6.3408699  65.211        2.12347272         24.0  3.4222222
## 108  2.39593196 0.1847213  99.135        1.07132447         31.0  1.6400000
## 109  0.84639847 4.7084438  53.998        1.80343313         68.0  6.4555556
## 110  4.98659870 0.3624706  17.211        0.00000000         59.0  1.6000000
## 111  2.82589744 2.4482249  52.198        0.00000000          6.3  3.3000000
## 112  0.15676654 0.5038453  83.844        1.13389947         10.0  2.2500000
## 113  0.82347478 0.3825330  47.192        0.53906244        118.0  0.2000000
## 114  0.79831740 4.0191136  56.092        1.54868038         17.0  5.6125000
## 115  1.05987171 0.3522140  42.055        0.83772947        298.0  0.4000000
## 116 79.52998418 2.1766595 100.000        0.70577580         47.0  2.7457143
## 117  1.13290578 3.2223629  53.726        0.78819906          5.8  6.3400000
## 118  1.02639860 5.3152643  54.541        1.72227622          5.3  4.6555556
## 119  0.47630120 0.7184723  66.355        1.02339821        520.0  2.8000000
## 120  5.29652104 3.2120425  81.459        0.79306494         66.0 10.6080000
## 121  0.93529058 6.1672057  80.321        2.76446761          9.4  3.1888889
## 122  3.45558922 1.6065781  18.476        0.16478915         64.0  3.5500000
## 123  0.25001043 5.1726032  87.431        2.61847597          5.5  2.7666667
## 124  2.15521378 5.1493834  73.797        2.28867018          6.4  5.1222222
## 125  0.65572714 0.5234433  27.134        0.78207686         84.0  5.2666667
## 126  1.35897207 2.5315654  49.949        0.26030714        153.0  2.1000000
## 127  1.45046773 0.2700138  41.702        0.42289247         36.0  0.8000000
## 128  0.74441323 1.7197442  68.945        0.71183934         35.0  2.0545455
## 129  1.06960129 1.6774797  75.143        1.73570816         16.0  2.4555556
## 130  2.13061734 0.2057545  23.774        0.00000000        200.0  0.7500000
## 131  0.77029667 3.9876216  69.352        1.18842716         80.0  8.9222222
## 132  1.35609110 0.1307769  86.522        1.43897112          1.0  1.6777778
## 133  2.74827392 5.0282090  83.398        2.75158819          8.0  3.2111111
## 134  0.35766089 3.8751036  82.256        2.48855760          3.0  3.0333333
## 135  0.19708028 4.3947641  95.334        0.86794498         33.0  2.5250000
## 136  0.77469205 0.8369038  50.478        0.15375896         70.0  4.5888889
## 137  0.53977853 0.3498816  36.642        0.45632576         48.0  0.6900000
## 138  0.23341479 0.2529708  43.521        0.14718638        346.0  1.9500000
## 139  0.37324591 0.4222739  32.209        0.10620013        210.0  2.3500000
##     TempMarzo         PBI
## 1        7.60   1.8351696
## 2        6.04  11.3351950
## 3       17.91  14.1967389
## 4       22.78   6.7205961
## 5       17.51  20.0684923
## 6       -0.57   8.3491802
## 7       25.37  45.7525548
## 8        1.42  48.9687140
## 9        4.97  17.0906963
## 10      25.42   3.3061083
## 11      -0.69  18.1721809
## 12       5.23  45.2631622
## 13      24.45   8.0937796
## 14      30.14   2.0675705
## 15       5.68   8.3417246
## 16      22.07   6.5317860
## 17       4.01  11.6971771
## 18      24.30  16.1336867
## 19      25.49  15.5847506
## 20      25.92  80.8004129
## 21       4.70  17.9465777
## 22      30.63   1.6709928
## 23      20.43   0.7568378
## 24      27.93   3.3333517
## 25      26.27   3.2930885
## 26     -18.72  44.2264907
## 27      26.96   0.8532664
## 28      25.14  13.2116325
## 29      25.20   2.6420144
## 30      25.60   5.6636489
## 31       5.83  22.9922117
## 32       2.80  32.7718057
## 33       2.24  48.5249925
## 34      25.75   2.7442687
## 35      22.88  13.9050879
## 36      21.93  10.8759023
## 37      17.83  10.8110341
## 38      25.70   7.2348467
## 39      25.04  30.5908474
## 40      -2.35  28.8340001
## 41      23.48   1.5126247
## 42      25.00   8.8754441
## 43      -6.09  42.8552430
## 44       6.37  40.3515680
## 45      26.14  17.0778163
## 46      27.32   2.4051919
## 47       0.72   9.5834608
## 48       3.87  46.5762068
## 49      29.52   3.9219764
## 50       8.19  27.2065489
## 51      22.94   7.5159291
## 52      27.63   2.0022876
## 53      25.65   7.1987245
## 54      23.47   1.7058976
## 55      23.38   4.4336960
## 56       5.44  25.7573736
## 57      26.55   2.0073216
## 58      10.87  22.2523960
## 59       0.49  13.5313781
## 60      23.45   5.8366566
## 61      25.79  10.5772045
## 62      11.33  18.4501839
## 63      15.12  15.8895142
## 64       6.00  59.3055780
## 65      14.96  34.5710854
## 66       6.52  37.7630816
## 67      23.44   8.5085469
## 68       2.52  39.1530060
## 69      13.08   9.2014965
## 70      -3.96  24.1030338
## 71      26.10   2.8839337
## 72      19.23  75.8307296
## 73      -1.37  23.8146826
## 74      10.39  13.2450688
## 75      15.13   2.9043623
## 76      26.44   1.2563111
## 77      17.76  19.3396159
## 78      -0.47  27.8087523
## 79       4.70 100.2191161
## 80      23.99   1.7044431
## 81      23.25   1.1836484
## 82      25.19  25.8714034
## 83      27.34   2.0131236
## 84      25.29   3.8172107
## 85      25.48  19.3987280
## 86      18.44  17.8555895
## 87       2.93   5.8933274
## 88      -8.80  11.1277975
## 89       2.53  16.2734892
## 90      13.09   7.4795258
## 91      25.48   1.2616857
## 92      22.74   5.0214146
## 93      22.67  10.2230967
## 94      10.02   2.4752041
## 95       4.95  49.9843155
## 96      13.60  36.4995043
## 97      24.95   4.8837325
## 98      26.01   0.9278714
## 99      -5.49  62.6503184
## 100     23.40  42.4790524
## 101     16.01   4.6501098
## 102     25.44  20.8839055
## 103     25.84  11.4029323
## 104     19.99  12.3515166
## 105     25.13   7.0082665
## 106      2.65  25.9904310
## 107     11.33  29.3390041
## 108     21.78 123.2139364
## 109      3.40  21.6182719
## 110     19.25   1.7889788
## 111     25.90  10.9317631
## 112     20.60  51.5878305
## 113     28.47   3.1310271
## 114      4.91  14.9080484
## 115     27.65   1.4959394
## 116     28.62  86.0684237
## 117      2.40  29.0915206
## 118      3.38  31.7402860
## 119     21.10  12.8666891
## 120      3.66  34.6370853
## 121      8.60  34.5888453
## 122     27.03  11.1185327
## 123     -4.98  47.6285919
## 124      0.09  61.3146089
## 125     -3.08   2.7293337
## 126     27.38  15.8571484
## 127     29.31   1.4956789
## 128     14.21  11.2660561
## 129      4.77  23.5212137
## 130     23.68   1.8103287
## 131      1.18   8.4417522
## 132     22.61  65.5180899
## 133      4.66  41.1611271
## 134      0.06  55.0581658
## 135     21.26  20.4797528
## 136      5.38   6.8361061
## 137     20.82   3.5314239
## 138     22.90   3.8025013
## 139     22.92   2.5606953

1.Analizar la relación entre variables**

Aunque hemos realizado un análisis exploratorio de datos, es importante como primer paso para establecer un modelo lineal múltiple es estudiar la relación que existe entre las variables seleccionadas arriba. Esta información es crítica a la hora de identificar cuáles pueden ser los mejores predictores para el modelo, qué variables presentan relaciones de tipo no lineal (por lo que no pueden ser incluidas) y para identificar colinialidad entre predictores.

round(cor(x = df_covid, method = "pearson"), 3)
##                   PoblaDens Pobla80 Urbano l10muertes.permil Tuberculosis
## PoblaDens             1.000   0.011  0.127            -0.038       -0.054
## Pobla80               0.011   1.000  0.469             0.668       -0.481
## Urbano                0.127   0.469  1.000             0.572       -0.374
## l10muertes.permil    -0.038   0.668  0.572             1.000       -0.481
## Tuberculosis         -0.054  -0.481 -0.374            -0.481        1.000
## Camas                 0.004   0.714  0.370             0.364       -0.331
## TempMarzo             0.115  -0.681 -0.322            -0.511        0.367
## PBI                   0.263   0.452  0.665             0.539       -0.405
##                    Camas TempMarzo    PBI
## PoblaDens          0.004     0.115  0.263
## Pobla80            0.714    -0.681  0.452
## Urbano             0.370    -0.322  0.665
## l10muertes.permil  0.364    -0.511  0.539
## Tuberculosis      -0.331     0.367 -0.405
## Camas              1.000    -0.712  0.368
## TempMarzo         -0.712     1.000 -0.350
## PBI                0.368    -0.350  1.000
ggcorr(df_covid, method = c("everything", "pearson")) 

Del análisis preliminar se pueden extraer las siguientes conclusiones:

Las variables que tienen una mayor relación lineal con la mortalidad son: poblacion 80 (r= 0.68), urbano (r= -0.57) y PBI (r= 0.54). La poblacion de 80 y las camas están medianamente correlacionados (r = 0.7) por lo que posiblemente no sea útil introducir ambos predictores en el modelo. Lo mismo sucede para la temperatura y las camas

Para realizar un primer modelo de regresion lineal utilizaremos la variable de Pobla80 ya que presenta una correlación alta y considerando el contexto que se trata de la población de mayor riesgo frente al COVID-19

2.Generar el modelo

Crearemos un modelo lineal considerando como variable de respuesta l10muertes.permil y regresoras Pobla80-PoblaDens-Urbano-Tuberculosis-Camas -TempMarzo -PBI

modelo <- lm(l10muertes.permil ~ Pobla80+PoblaDens+Urbano+Tuberculosis+Camas+TempMarzo+PBI , data = df_covid )

summary(modelo)
## 
## Call:
## lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + 
##     Tuberculosis + Camas + TempMarzo + PBI, data = df_covid)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.08088 -0.29012 -0.03314  0.27929  1.29267 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.5981046  0.2012630   2.972  0.00352 ** 
## Pobla80       0.2040913  0.0338369   6.032 1.55e-08 ***
## PoblaDens    -0.0111873  0.0063386  -1.765  0.07990 .  
## Urbano        0.0082702  0.0025767   3.210  0.00167 ** 
## Tuberculosis -0.0005815  0.0003431  -1.695  0.09246 .  
## Camas        -0.1101291  0.0263087  -4.186 5.17e-05 ***
## TempMarzo    -0.0143117  0.0057068  -2.508  0.01337 *  
## PBI           0.0062466  0.0027181   2.298  0.02314 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4765 on 131 degrees of freedom
## Multiple R-squared:  0.6239, Adjusted R-squared:  0.6038 
## F-statistic: 31.04 on 7 and 131 DF,  p-value: < 2.2e-16

Análisis preliminar, esta regresión es capaz de explicar el 62,3% de lo que ocurre en la regresión,presenta un p-valor pequeño y un RSS de 0.476. Nuestro modelo es: \[\hat{l10muertes.pormil} = 0.59+0.20\hat{Pobla80}-0.01\hat{PoblaDens}\] \[+0.008\hat{Urbano}-0.0005\hat{Tuberculosis}\] \[-0.11\hat{Camas}-0.014\hat{TempMarzo}+0.006\hat{PBI}\]

El intercept es de signo positivo por lo que nos indica una pendiente creciente, aquellos estimadores con signo negativo tienen sentido dado que si uno tiene menor densidad población debería tener menos riesgo de contacto porque hay menor concentración de personas, lo mismo sucede con las camas mayor cantidad de camas, más pacientes pueden ser atendidos y menos muertes se generarias, con la temperatura considerando que es una enfermedad del tipo respiratoria auqellos países que no esten en invieron u otoño presentarán menos casos que los que si se encuentren, por otro lado mayor poblacion adulta implica mayor cantidad de muertos por ser el grupo de riesgo y el PBI al ser un indicador económico uno podría asumir que países con PBI bajo no tendrán buenos sistemas de salud.

3.Identificación de posibles valores atípicos o influyentes

df_covid$studentized_residual <- rstudent(modelo)
ggplot(data = df_covid, aes(x = predict(modelo), y = abs(studentized_residual))) +
geom_hline(yintercept = 3, color = "grey", linetype = "dashed") +
# se identifican en rojo observaciones con residuos estandarizados absolutos > 3
geom_point(aes(color = ifelse(abs(studentized_residual) > 3, 'red', 'black'))) +
scale_color_identity() +
labs(title = "Distribución de los residuos studentized",
     x = "predicción modelo") + 
theme_bw() + theme(plot.title = element_text(hjust = 0.5))

which(abs(df_covid$studentized_residual) > 3)
## integer(0)

Realizando el gráfico de los residuos estandarizados y nuestro test para idetificar valores atipicos se concluye que no se encuentra ninguno. Sin embargo el gráfico presenta una leve concentración en la esquina izquierda correspondiente a valores pequeños hasta el 0.5 de predicción y luego se los ve dispersos.

Vamos a generar una tabla que nos permita cuantificar la influencia de las observaciones que son significativamente influyentes en nuestro predictor.

summary(influence.measures(modelo))
## Potentially influential observations of
##   lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano +      Tuberculosis + Camas + TempMarzo + PBI, data = df_covid) :
## 
##     dfb.1_ dfb.Pb80 dfb.PblD dfb.Urbn dfb.Tbrc dfb.Cams dfb.TmpM dfb.PBI
## 26  -0.05   0.00    -0.01    -0.01    -0.01     0.05     0.08    -0.01  
## 36   0.01   0.01    -0.03     0.13    -0.18    -0.07     0.09    -0.15  
## 68   0.59  -0.23     0.02    -0.03    -0.10    -0.76    -0.54     0.09  
## 75  -0.03  -0.05    -0.01     0.06    -0.23     0.05     0.07    -0.05  
## 79   0.00  -0.01    -0.03    -0.04     0.02     0.00     0.00     0.12  
## 87   0.26  -0.24     0.08    -0.15    -0.09     0.31    -0.18    -0.12  
## 88  -0.08   0.29    -0.05    -0.16    -0.29    -0.11     0.36     0.08  
## 96   0.07  -0.17     0.09    -0.16     0.03     0.19    -0.03    -0.01  
## 108 -0.01   0.30     0.28     0.18    -0.03    -0.01    -0.09    -0.89  
## 116  0.12   0.03    -2.51_*  -0.11    -0.06     0.07     0.09     0.03  
## 120  0.01   0.01     0.00     0.00     0.00    -0.02     0.00     0.00  
##     dffit   cov.r   cook.d hat    
## 26  -0.08    1.25_*  0.00   0.15  
## 36   0.38    0.68_*  0.02   0.02  
## 68  -1.02_*  1.08    0.13   0.22_*
## 75  -0.26    1.19_*  0.01   0.13  
## 79   0.14    1.22_*  0.00   0.13  
## 87   0.59    0.72_*  0.04   0.05  
## 88  -0.58    1.17    0.04   0.17_*
## 96  -0.40    0.79_*  0.02   0.03  
## 108 -1.00_*  1.30_*  0.12   0.29_*
## 116 -2.68_* 18.90_*  0.90   0.95_*
## 120 -0.03    1.22_*  0.00   0.13

La visualización gráfica de las influencias se obtiene:

influencePlot(modelo)

##        StudRes        Hat      CookD
## 36   2.8089529 0.01799481 0.01717000
## 68  -1.9489237 0.21623105 0.12824793
## 87   2.7010994 0.04527254 0.04126294
## 108 -1.5498732 0.29469147 0.12412726
## 116 -0.6456102 0.94517926 0.90231428

Los análisis muestran varias observaciones influyentes (posición 116 , 108 y 68) que exceden los límites de preocupación para los valores de Leverages o Distancia Cook. Estudios más exhaustivos consistirían en rehacer el modelo sin las observaciones y ver el impacto.

Los gráficos de la distancia de Cook sirven para detectar observaciones que influyen fuertemente en los valores ajustados del modelo.

Los análisis muestran varias observaciones influyentes que exceden los límites de preocupación para los valores de Leverages o Distancia Cook

**Posicion 116 = Singapurm 108 = Qatar y 68 = Japon

Análisis de residuos

1- Relación lineal entre el predictor numérico y la variable respuesta

Podemos validar que sucede con esta condición utilizando un scatterplot entre la cantidad de muertes y las distintas features seleccionadas

Poblacion de 80

PoblaDens+Urbano+Tuberculosis+Camas+TempMarzo+PBI

plot1 <- ggplot(data = df_covid, aes(Pobla80, modelo$residuals)) +
    geom_point() + geom_smooth(color = "firebrick") + geom_hline(yintercept = 0) +
    theme_bw()

plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Los residuos se encuentran en su mayoría concentrados entre el 0 y 2 de Pobla80y entre el 0.5 y -05 de los residuos estandarizados, , la variable es constante hasta que cae. Podemos decir que se cumple la linealidad para el predictor seleccionado.

Poblacion Densidad

plot1 <- ggplot(data = df_covid, aes(PoblaDens, modelo$residuals)) +
    geom_point() + geom_smooth(color = "firebrick") + geom_hline(yintercept = 0) +
    theme_bw()

plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Los residuos se encuentran en su mayoría concentrados en el cuadrante izquierdo del grafico, Podemos decir que no se cumple la linealidad para el predictor seleccionado.

Urbano

plot1 <- ggplot(data = df_covid, aes(Urbano, modelo$residuals)) +
    geom_point() + geom_smooth(color = "firebrick") + geom_hline(yintercept = 0) +
    theme_bw()

plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Los residuos se encuentran en su mayoría distribuidos aleatoriamente entorno al 0, la variable es constante Podemos decir que se cumple la linealidad para el predictor seleccionado.

Tuberculosis

plot1 <- ggplot(data = df_covid, aes(Tuberculosis, modelo$residuals)) +
    geom_point() + geom_smooth(color = "firebrick") + geom_hline(yintercept = 0) +
    theme_bw()

plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Los residuos se encuentran en su mayoría concentrados entre el 0 y 100 de Tuberculosisy entre el 1 y -1 de los residuos estandarizados, la variable es constante a lo largo de todos los valores. Podemos decir que se cumple la linealidad para el predictor seleccionado.

Camas

plot1 <- ggplot(data = df_covid, aes(Camas, modelo$residuals)) +
    geom_point() + geom_smooth(color = "firebrick") + geom_hline(yintercept = 0) +
    theme_bw()

plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Los residuos se encuentran en su mayoría dsiperos entre los valores 0 a 5 de Camas, la variable es constante hasta que cae. Podemos decir que se cumple la linealidad para el predictor seleccionado.

Temprartura Marzo

plot1 <- ggplot(data = df_covid, aes(TempMarzo, modelo$residuals)) +
    geom_point() + geom_smooth(color = "firebrick") + geom_hline(yintercept = 0) +
    theme_bw()

plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Los residuos se encuentran en su mayoría distribuidos a lo largo del cero podemos ver como dos grandes grupos que podrían ser consecuencia de que el hemisferio Sur y el hemisferio norte presentan estaciones opuestas.Podemos decir que se cumple la linealidad para el predictor seleccionado.

PBI

plot1 <- ggplot(data = df_covid, aes(PBI, modelo$residuals)) +
    geom_point() + geom_smooth(color = "firebrick") + geom_hline(yintercept = 0) +
    theme_bw()

plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Los residuos se encuentran en su mayoría concentrados entre el 0 y 25 de PBIy entre el 0.5 y -0.5 de los residuos estandarizados,, la variable es constante hasta que cae consecuencia de un punto. Podemos decir que se cumple la linealidad para el predictor seleccionado, si consideramos que hay puntos muy disperos.

2- Distribución normal de los residuos

Vamos a comparar los cuantiles de la distribución observada con los cuantiles teóricos de una distribución normal con la misma media y desviación estándar que los datos.

qqnorm(modelo$residuals)
qqline(modelo$residuals)

ks.test(modelo$residuals,"pnorm",mean(modelo$residuals),sd(modelo$residuals))
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  modelo$residuals
## D = 0.048945, p-value = 0.8931
## alternative hypothesis: two-sided

El análisis gráfico confirman la normalidad, debido a que estos se encuentran en su mayoria alienados entorno a la recta, con una leve “desprendimiento” en las colas, Realizando el test de bondad de ajuste dado que mi p-value es de 0.89, estoy en condiciones de rechazar mi hipotesis nula y afirmar que tienen distribucion normal

3-Variabilidad constante de los residuos (homocedasticidad)

Vamos a graficar los valores ajustado por el modelo y los residuos.

ggplot(data = df_covid, aes(modelo$fitted.values, modelo$residuals)) +
geom_point() +
geom_smooth(color = "firebrick", se = FALSE) +
geom_hline(yintercept = 0) +
theme_bw()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

#bptest(modelo)

Como la distribución de los residuos frente a los valores ajustados por el modelo, se distribuyen distribuir de forma aleatoria en torno a cero, pero en forma de huevo , se da cuando la variable de respuesta es una proporción.Lo que esta pasando es que estamos violando el principio de heterocedasticidad que habla de que la varianza de todos los errores sea igual.

Para saber si tengo un problema de heterocedasticidad puedo realizar un test para saber realmente si pasa. Lo que voy a testear es que la varianza de cada uno de mis errores es la misma vs la hipótesis alternativa de que hay alguno distinto

cor.test(abs(modelo$residuals),modelo$fitted.values,method="spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  abs(modelo$residuals) and modelo$fitted.values
## S = 367626, p-value = 0.0355
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1786362

No hay un problema de heterosticidad dado que el p-value no nos dió un valor tan pequeño

4- No multicolinialidad

Matriz de correlación entre predictores.

corrplot(cor(dplyr::select(df_covid, PoblaDens, Pobla80,Urbano, l10muertes.permil,Tuberculosis,Camas, TempMarzo,PBI, l10muertes.permil)),
         method = "number", tl.col = "black")

5 -Análisis de Inflación de Varianza (VIF)

vif(modelo)
##      Pobla80    PoblaDens       Urbano Tuberculosis        Camas    TempMarzo 
##     2.772879     1.141569     1.934049     1.397990     2.567937     2.438824 
##          PBI 
##     2.091513

No hay predictores que muestren una correlación lineal muy alta ni inflación de varianza.

6- Autocorrelacion

dwt(modelo, alternative = "two.sided")
##  lag Autocorrelation D-W Statistic p-value
##    1      0.06317665      1.870852    0.41
##  Alternative hypothesis: rho != 0

No hay evidencia de autocorrelación

Análisis PBI

El PBI presenta una correlación fuerte y positiva, con las muertes las camas de hospitales y la población de 80, lo que hace sentido dado que podríamos asumir que aquellos países que presentan un menor PBI pueden no contar con buenos sistemas de salud lo que podría ocacionar mayor cantidad de muertes.

Vamos a crear una serie de columnas nuevas correspondientes a diversas interacciones con el PBI

modelo_pbi1 <- lm(l10muertes.permil ~ PoblaDens+Pobla80*PBI+Urbano*PBI+Tuberculosis*PBI+PBI*Camas+TempMarzo+PBI, data = df_covid )

summary(modelo_pbi1)
## 
## Call:
## lm(formula = l10muertes.permil ~ PoblaDens + Pobla80 * PBI + 
##     Urbano * PBI + Tuberculosis * PBI + PBI * Camas + TempMarzo + 
##     PBI, data = df_covid)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.12733 -0.27284 -0.02685  0.32828  1.29753 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.623e-01  2.673e-01   1.355 0.177753    
## PoblaDens        -1.013e-02  6.855e-03  -1.477 0.142099    
## Pobla80           8.080e-02  6.990e-02   1.156 0.249859    
## PBI               2.544e-02  1.489e-02   1.709 0.089951 .  
## Urbano            1.048e-02  3.037e-03   3.452 0.000756 ***
## Tuberculosis     -5.342e-04  5.173e-04  -1.033 0.303647    
## Camas            -4.365e-02  5.693e-02  -0.767 0.444613    
## TempMarzo        -9.900e-03  6.371e-03  -1.554 0.122712    
## Pobla80:PBI       3.725e-03  2.116e-03   1.760 0.080817 .  
## PBI:Urbano       -2.084e-04  1.534e-04  -1.359 0.176667    
## PBI:Tuberculosis -1.722e-05  4.906e-05  -0.351 0.726177    
## PBI:Camas        -2.408e-03  1.893e-03  -1.273 0.205515    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4743 on 127 degrees of freedom
## Multiple R-squared:  0.6387, Adjusted R-squared:  0.6074 
## F-statistic: 20.41 on 11 and 127 DF,  p-value: < 2.2e-16

Podemos observar un leve incremento en el \(R^2\) lógico porque sumamos una feature. Dado que esto no nos es representativo veamos que pasa con los residuos

par(mfrow=c(2,2))
plot(modelo_pbi1)

Realizando interacciones con el PBI se logran mejorar las salidas no pareciera que afectara a los residuos

4. Seleccion de un modelo

Cosndierando como criterios de evaluación el \(R^2, p-value, RSS\) y sus residuos, el mejor modelo que tenemos es aquel que considera las variables sin ninguna interaccion con el PBI, en otras palabras:

\[\hat{l10muertes.pormil} = 0.59+0.20\hat{Pobla80}-0.01\hat{PoblaDens}\] \[+0.008\hat{Urbano}-0.0005\hat{Tuberculosis}\] \[-0.11\hat{Camas}-0.014\hat{TempMarzo}+0.006\hat{PBI}\]

El modelo con todas las variables introducidas como predictores tiene un \(R^2\) alta (0.6239), es capaz de explicar el 62,39% de la variabilidad observada en la mortalidad logarimica por millon de habitantes para el covid-19. El \(p-value\) del modelo es significativo (\(2.2e^{-16}\)) por lo que se puede aceptar que el modelo no es por azar, al menos uno de los coeficientes parciales de regresión es distinto de 0. Muchos de ellos no son significativos, lo que es un indicativo de que podrían no contribuir al modelo.

Colinealidad

Varios de los datos con los que se cuentan debido a como fueron cosnturidos y lo que representan, contarán con una alta colinealidad. De este conocimiento previo podemos identifcar los siguientes casos:

  • Todo lo que represente porcentaje poblacional, tiene una relación con la población total (PoblaData). A su vez muchos de estos también tienen una relacion entre ellos porque en algunos casos estan incluidos en ese porcentaje, el caso del porcentaje de la población de 65 años incluye a la población de 80.

  • Por como esta construida Pobla80 que es un promedio entre Female80 y Male80.

  • La densidad poblaciónal tiene que tener una correlación fuerte con la poblacion total dado que es un inidcador que se contruye como población total / superficie

  • El PBI en este caso representa el PPP y su construcción es el PBI/cantidad total de habitantes

  • HipTen por consturcción es un promedio de HT.women y HT.men

  • l10muertes.permil por construcción donde muertes.permil es el número de muertos cada millón de habitantes.

  • BCGy BCGfrepresentan lo mismo dado que es la variables BCG escrita como un factor.

Tenemos varias formas de corroborar lo que mencionamos una podría ser viendo la correlación entre estas variables y otra generando un modelo de regresión con estos predictores y observar el\(R^2\)

# Excluimos las variables categoricas
df_covid_total = data.frame(Hombres80, Mujeres80, Pobla80, Pobla65, PoblaMid, PoblaData, PoblaDens, Mujeres, Urbano,ExpectVida, NeontlMort, DisMort, Lesion, EnfNoTrans,Tuberculosis,Diabetes, ImmunSaramp, HipTen.H, HipTen.M, BCG,Medicos,Camas,PBI,TempMarzo,l10muertes.permil,muertes.permil)

df_covid_total
##      Hombres80  Mujeres80   Pobla80   Pobla65 PoblaMid   PoblaData   PoblaDens
## 1   0.23332582  0.3209823 0.2771541  2.584927 54.32490  0.37172386  0.56937760
## 2   2.44807869  3.0339539 2.7410163 13.744736 68.58239  0.02866376  1.04612263
## 3   1.18175550  1.3602137 1.2709846  6.362497 63.48882  0.42228429  0.17730075
## 4   0.20753263  0.3372488 0.2723907  2.216374 50.97470  0.30809762  0.24713052
## 5   1.77481880  3.4475320 2.6111754 11.117789 64.12128  0.44494502  0.16258510
## 6   2.45959960  3.7882699 3.1239347 11.253818 68.11276  0.02951776  1.03680225
## 7   3.40801882  4.6792491 4.0436339 15.656475 65.15291  0.24992369  0.03249129
## 8   3.92008681  6.5538665 5.2369767 19.001566 66.70049  0.08847037  1.07206927
## 9   0.98233980  1.7371450 1.3597424  6.195183 70.43525  0.09942334  1.20265320
## 10  0.92567665  1.0671409 0.9964088  5.158391 67.13559  1.61356039 12.39579312
## 11  1.98505780  5.6239517 3.8045048 14.845148 68.28891  0.09485386  0.46728800
## 12  4.26847412  7.1212019 5.6948380 18.788744 64.15583  0.11422068  3.77214927
## 13  1.04770490  1.0313461 1.0395255  4.736459 64.98378  0.00383071  0.16793994
## 14  0.33308679  0.5400435 0.4365651  3.253605 54.29871  0.11485048  1.01853920
## 15  1.20832395  1.2107077 1.2095158  6.003012 68.22563  0.00754394  0.19777528
## 16  1.36894924  1.8807749 1.6248620  7.191947 61.73450  0.11353142  0.10480146
## 17  2.62930836  4.1430706 3.3861895 16.470317 68.76346  0.03323929  0.64920488
## 18  0.32968114  0.6582073 0.4939442  4.223874 61.66318  0.02254126  0.03977425
## 19  1.38043099  2.2316251 1.8060280  8.922838 69.74309  2.09469333  0.25061716
## 20  0.68594552  0.8308421 0.7583938  4.873148 72.10039  0.00428962  0.81396964
## 21  3.47833930  5.9662322 4.7222857 21.021914 64.38262  0.07024216  0.64703537
## 22  0.17806863  0.3125849 0.2453268  2.406981 52.64494  0.19751535  0.72191283
## 23  0.22728873  0.3563006 0.2917947  2.246940 52.25138  0.11175378  4.35178271
## 24  0.44915480  0.6916225 0.5703887  4.568680 64.22991  0.16249798  0.92056413
## 25  0.24541433  0.3712980 0.3083562  2.728877 54.63954  0.25216237  0.53343989
## 26  3.44384968  5.1704175 4.3071336 17.232007 66.89774  0.37058856  0.04075308
## 27  0.22803331  0.4776179 0.3528256  2.825774 52.87991  0.04666377  0.07490412
## 28  1.48426928  2.0351814 1.7597254  8.478047 68.44406  0.49648685  0.44748702
## 29  0.31250447  0.4654681 0.3889863  3.007009 57.45457  0.00832322  4.47244478
## 30  0.20761885  0.3678366 0.2877278  2.681720 55.55478  0.05244363  0.15356846
## 31  3.64181309  7.4858273 5.5638202 20.445433 65.04263  0.04089400  0.73077198
## 32  2.83741960  5.3461832 4.0918014 19.420877 64.99252  0.10625695  1.37602888
## 33  3.56907492  5.4657626 4.5174188 19.812953 63.72878  0.05797446  1.38067302
## 34  0.51375834  0.7060595 0.6099089  4.527579 65.89830  0.00958920  0.41368421
## 35  1.41201959  1.7626442 1.5873319  7.082817 64.94005  0.10627165  2.19978576
## 36  1.31184512  1.7634434 1.5376442  7.157290 64.81412  0.17084357  0.68788682
## 37  0.59376508  0.9883030 0.7910340  5.229779 60.97150  0.98423595  0.98873469
## 38  1.64624432  2.0376142 1.8419292  8.287090 64.58083  0.06420744  3.09881467
## 39  0.23244491  0.3448975 0.2886712  2.457877 60.42552  0.01308974  0.46665740
## 40  3.14651675  7.9517097 5.5491132 19.626357 64.01657  0.01320884  0.30386105
## 41  0.45453976  0.5479606 0.5012502  3.501133 55.71589  1.09224559  1.09224559
## 42  0.47150751  0.6876126 0.5795601  5.449680 65.03770  0.00883483  0.48357033
## 43  3.92274373  6.8888011 5.4057724 21.720788 62.13403  0.05518050  0.18156856
## 44  4.56728528  7.6391664 6.1032258 20.034625 62.00891  0.66987244  1.22338396
## 45  0.38531522  0.6817970 0.5335561  3.563907 59.41022  0.02119275  0.08224764
## 46  0.24379309  0.2753184 0.2595557  2.589981 53.14127  0.02280102  2.25306522
## 47  2.51953653  4.8459528 3.6827447 14.865491 65.33927  0.03731000  0.65275202
## 48  5.07035282  8.1838074 6.6270801 21.461962 64.91701  0.82927922  2.37370970
## 49  0.27996116  0.3732648 0.3266130  3.068898 59.33504  0.29767108  1.30821429
## 50  6.13905854  8.3967659 7.2679122 21.655272 64.27348  0.10727668  0.83224732
## 51  0.93517499  1.1745195 1.0548473  4.812073 60.75253  0.17247807  1.60953779
## 52  0.25836523  0.3807578 0.3195615  2.926022 53.22380  0.12414318  0.50522212
## 53  1.07800787  1.4664646 1.2722362  6.450271 65.33518  0.00779004  0.03957348
## 54  0.68159397  0.9736716 0.8276328  4.949404 61.80835  0.11123176  4.03598549
## 55  0.85537137  1.2034197 1.0293956  4.690618 63.56850  0.09587522  0.85687032
## 56  2.66613364  5.8764855 4.2713096 19.157725 66.43028  0.09768785  1.07906606
## 57  0.26534733  0.3608357 0.3130915  2.480519 50.39318  0.15477751  0.12291734
## 58  1.98522693  3.4164040 2.7008155 11.529802 68.71630  0.18729160  0.25189446
## 59  1.39566195  2.1799124 1.7877871 10.920884 71.20211 13.92730000  1.48348833
## 60  0.81547194  1.0741411 0.9448065  6.179956 66.76674 13.52617328  4.54938073
## 61  0.63862200  1.0737409 0.8561815  5.857166 67.59164  2.67663435  1.47752190
## 62  1.27963767  0.9669791 1.1233084  6.184574 69.33887  0.81800269  0.50222420
## 63  0.37591930  0.5902882 0.4831038  3.323600 58.28983  0.38433600  0.88530570
## 64  2.50008058  3.5376243 3.0188525 13.865802 64.72778  0.04853506  0.70452983
## 65  2.41256950  3.6798039 3.0461867 11.976986 60.09777  0.08883800  4.10526802
## 66  5.48160915  8.8652623 7.1734357 22.751680 63.91920  0.60431283  2.05450748
## 67  1.93886184  2.1524976 2.0456797  8.796643 67.45326  0.02934855  2.70993075
## 68  6.16625352 10.5564415 8.3613475 27.576370 59.72678  1.26529100  3.47073458
## 69  0.53354023  0.6712123 0.6023762  3.846490 61.90802  0.09956011  1.12142498
## 70  0.91633260  2.0700761 1.4932044  7.391846 64.14760  0.18276499  0.06769826
## 71  0.19255206  0.3472758 0.2699139  2.339187 57.87865  0.51393010  0.90299417
## 72  0.20842979  0.2570540 0.2327419  2.550472 75.91064  0.04137309  2.32172222
## 73  3.14242031  7.9881716 5.5652960 20.043620 63.96060  0.01926542  0.30983307
## 74  1.19514163  1.7705697 1.4828557  7.002368 66.90176  0.06848925  6.69494135
## 75  0.37851920  1.2632823 0.8209008  4.901087 62.38256  0.02108132  0.69437813
## 76  0.33809045  0.4611975 0.3996440  3.253432 55.62158  0.04818977  0.50030907
## 77  0.62399259  0.8970569 0.7605248  4.392040 67.28872  0.06678567  0.03795632
## 78  3.50274874  8.1713074 5.8370281 19.705033 65.41254  0.02789533  0.44531351
## 79  3.00125285  5.0458011 4.0235270 14.183154 69.93802  0.00607728  2.50093827
## 80  0.38401050  0.4643295 0.4241700  2.986717 56.34513  0.26262368  0.45139856
## 81  0.20543741  0.4120391 0.3087382  2.645435 53.45228  0.18143315  1.92440762
## 82  1.01429906  1.1340407 1.0741699  6.671755 69.33310  0.31528585  0.95962821
## 83  0.24060395  0.2961390 0.2683715  2.507230 49.94928  0.19077690  0.15635016
## 84  0.32220470  0.5186323 0.4204185  3.141112 56.77571  0.04403319  0.04272164
## 85  1.45222816  2.5933641 2.0227961 11.474173 70.73213  0.01265303  6.23301970
## 86  1.32388392  1.7807238 1.5523039  7.223685 66.21947  1.26190788  0.64914626
## 87  1.43488921  3.0996156 2.2672524 11.469556 72.67035  0.03545883  1.23519804
## 88  0.44312835  0.8136545 0.6283914  4.083539 65.50689  0.03170208  0.02040609
## 89  2.64987500  4.1910961 3.4204855 14.974937 66.81554  0.00622345  0.46271004
## 90  0.89534738  1.4420998 1.1687236  7.012905 65.78072  0.36029138  0.80728519
## 91  0.22502107  0.4712336 0.3481273  2.890764 52.43844  0.29495962  0.37508535
## 92  0.57207835  0.9861366 0.7791075  5.784642 67.84431  0.53708395  0.82238615
## 93  0.40544266  0.8249161 0.6151794  3.636032 59.45372  0.02448255  0.02973746
## 94  0.73815416  0.7650848 0.7516195  5.727671 63.85806  0.28087871  1.95939107
## 95  3.64646480  5.7488137 4.6976392 19.196193 64.69565  0.17231017  5.11457910
## 96  3.17823477  4.2839926 3.7311137 15.652425 64.69414  0.04885500  0.18554176
## 97  0.87536492  1.3297727 1.1025688  5.247497 64.55082  0.06465513  0.53727048
## 98  0.20098806  0.2671884 0.2340882  2.595008 47.42067  0.22442948  0.17717651
## 99  3.20399502  5.2423112 4.2231531 17.049222 65.40174  0.05314336  0.14554920
## 100 0.29164290  0.6276245 0.4596337  2.392787 75.36071  0.04829483  0.15604145
## 101 0.66816849  0.6409555 0.6545620  4.312774 60.41741  2.12215030  2.75289319
## 102 1.69079701  2.1502704 1.9205337  8.104731 64.83296  0.04176873  0.56186077
## 103 1.01873059  1.4083568 1.2135437  6.430215 64.12928  0.06956071  0.17508359
## 104 1.46155999  1.9002429 1.6809015  8.088393 66.12100  0.31989256  0.24991606
## 105 0.52949911  1.0492596 0.7893793  5.122569 63.91439  1.06651922  3.57688305
## 106 2.90312619  6.0231067 4.4631164 17.517817 67.42991  0.37978548  1.24035886
## 107 4.92825496  7.7534849 6.3408699 21.953858 64.58823  0.10281762  1.12239454
## 108 0.08736843  0.2820742 0.1847213  1.370070 85.08917  0.02781677  2.39593196
## 109 3.42296861  5.9939190 4.7084438 18.338701 66.12674  0.19473936  0.84639847
## 110 0.28365324  0.4412880 0.3624706  2.938196 57.08623  0.12301939  4.98659870
## 111 2.20804973  2.6884001 2.4482249  9.589787 67.86891  0.00110210  2.82589744
## 112 0.39373448  0.6139561 0.5038453  3.314088 71.64306  0.33699947  0.15676654
## 113 0.31785259  0.4472135 0.3825330  3.086824 53.85775  0.15854360  0.82347478
## 114 3.14271267  4.8955145 4.0191136 18.345793 65.96453  0.06982084  0.79831740
## 115 0.27619394  0.4282340 0.3522140  2.966556 55.97380  0.07650154  1.05987171
## 116 1.57583078  2.7774882 2.1766595 11.463380 76.25834  0.05638676 79.52998418
## 117 2.06381766  4.3809082 3.2223629 15.629247 68.92462  0.05447011  1.13290578
## 118 3.49460207  7.1359265 5.3152643 19.606880 65.37135  0.02067372  1.02639860
## 119 0.46387285  0.9730718 0.7184723  5.318005 65.60251  0.57779622  0.47630120
## 120 2.07437980  4.3497052 3.2120425 14.418556 72.60812  0.51635256  5.29652104
## 121 4.67746877  7.6569426 6.1672057 19.378508 65.95449  0.46723749  0.93529058
## 122 1.29445871  1.9186975 1.6065781 10.473220 65.32978  0.21670000  3.45558922
## 123 4.08274767  6.2624588 5.1726032 20.095525 62.32269  0.10183175  0.25001043
## 124 3.98029809  6.3184688 5.1493834 18.623217 66.46583  0.08516543  2.15521378
## 125 0.44981821  0.5970683 0.5234433  3.021888 60.19383  0.09100837  0.65572714
## 126 2.07568057  2.9874503 2.5315654 11.900893 71.01212  0.69428524  1.35897207
## 127 0.23607069  0.3039569 0.2700138  2.869468 55.79601  0.07889094  1.45046773
## 128 1.43186270  2.0076257 1.7197442  8.315679 67.51420  0.11565204  0.74441323
## 129 1.25823610  2.0967233 1.6774797  8.483213 66.86738  0.82319724  1.06960129
## 130 0.13547819  0.2760308 0.2057545  1.940987 51.12849  0.42723139  2.13061734
## 131 2.37050281  5.6047403 3.9876216 16.434686 67.75290  0.44622516  0.77029667
## 132 0.09768939  0.1638645 0.1307769  1.085001 84.31149  0.09630959  1.35609110
## 133 4.13800199  5.9184161 5.0282090 18.395866 63.92605  0.66488991  2.74827392
## 134 3.10021646  4.6499908 3.8751036 15.807654 65.48331  3.27167434  0.35766089
## 135 2.71338358  6.0761446 4.3947641 14.814520 64.57750  0.03449299  0.19708028
## 136 0.62822093  1.0455867 0.8369038  4.419138 66.89480  0.32955400  0.77469205
## 137 0.28362786  0.4161354 0.3498816  2.876270 57.50884  0.28498687  0.53977853
## 138 0.15468651  0.3512552 0.2529708  2.099678 52.96418  0.17351822  0.23341479
## 139 0.24497323  0.5995746 0.4222739  2.939524 54.65941  0.14439018  0.37324591
##      Mujeres  Urbano ExpectVida NeontlMort DisMort Lesion EnfNoTrans
## 1   48.63585  25.495     62.701       37.1    29.8   19.5       44.1
## 2   49.06309  60.319     76.601        6.5    17.0    4.0       93.1
## 3   49.48427  72.629     75.307       14.6    14.2    9.5       75.7
## 4   50.53046  65.514     57.677       28.5    16.5    9.2       27.4
## 5   51.23735  91.870     72.924        6.4    15.8    6.5       77.6
## 6   52.95658  63.149     71.115        6.5    22.3    3.9       93.3
## 7   50.19962  86.012     80.400        2.3     9.1    5.9       89.5
## 8   50.82943  58.297     79.300        2.1    11.4    5.2       92.2
## 9   50.11575  55.680     70.128       11.2    22.2    4.6       86.6
## 10  49.38730  36.632     70.409       17.1    21.6    7.5       66.9
## 11  53.45605  78.595     69.300        1.3    23.7    7.0       90.5
## 12  50.59332  98.001     79.000        2.0    11.4    6.4       85.7
## 13  50.19252  45.724     71.533        8.6    22.1   13.2       67.4
## 14  50.09820  47.312     59.633       31.3    19.6   10.2       35.7
## 15  47.00264  40.895     70.822       16.4    23.3   10.5       68.6
## 16  49.78340  69.425     68.173       14.3    17.2   13.1       64.5
## 17  51.01054  48.245     74.622        4.1    17.8    3.7       94.5
## 18  51.73076  69.446     65.790       24.5    20.3    8.3       45.7
## 19  50.82992  86.569     71.804        8.1    16.6   12.2       73.9
## 20  48.03618  77.629     74.454        5.5    16.6    7.4       84.8
## 21  51.41409  75.008     71.300        3.6    23.6    2.6       95.2
## 22  50.09548  29.358     59.981       24.7    21.7   11.0       32.7
## 23  50.42199  13.032     59.092       21.7    22.9   12.1       32.1
## 24  51.19798  23.388     67.062       14.4    21.1   10.0       64.4
## 25  50.00349  56.374     57.235       26.6    21.6   10.9       35.2
## 26  50.39153  81.411     80.288        3.4     9.8    6.1       88.3
## 27  50.43647  41.364     50.152       41.2    23.1   10.3       26.0
## 28  50.92577  80.778     74.124        7.8    15.8   15.0       74.8
## 29  49.55870  28.965     62.210       31.6    22.9   10.9       41.7
## 30  50.06560  66.916     62.546       20.3    16.7    9.9       34.6
## 31  51.85262  56.947     74.900        2.6    16.7    5.3       92.4
## 32  50.80859  73.792     76.500        1.8    15.0    4.7       89.9
## 33  50.27420  87.874     79.200        3.1    11.3    3.8       89.7
## 34  47.36679  77.777     64.000       31.7    19.6   10.4       44.4
## 35  50.00780  81.074     70.609       19.4    19.0   12.0       72.3
## 36  49.97063  63.821     73.833        7.2    13.0   12.8       72.2
## 37  49.46997  42.704     69.453       11.2    27.7    5.8       84.1
## 38  53.11400  72.023     68.006        6.7    14.0   15.4       73.8
## 39  44.45585  72.143     57.059       29.9    22.0   10.7       35.9
## 40  52.85843  68.880     73.300        1.2    17.0    4.5       92.7
## 41  49.97889  20.763     63.997       28.1    18.3   11.7       39.3
## 42  49.30050  56.248     65.594       10.9    30.6    5.4       84.4
## 43  50.72076  85.382     78.600        1.0    10.2    5.5       93.2
## 44  51.58424  80.444     79.500        2.5    10.6    6.4       87.6
## 45  49.06862  89.370     63.879       21.0    14.4    9.1       41.0
## 46  50.40213  61.270     60.088       26.3    20.4   10.9       34.3
## 47  52.29124  58.632     68.980        5.9    24.9    3.6       93.7
## 48  50.66037  77.312     78.600        2.2    12.1    4.0       91.2
## 49  49.32583  56.060     62.437       23.9    20.8    9.8       42.7
## 50  50.91620  79.058     78.900        2.6    12.4    2.9       86.2
## 51  50.75935  51.054     70.830       12.3    14.9   15.7       59.2
## 52  51.82111  36.140     60.040       31.1    22.4    9.2       35.1
## 53  49.80802  26.606     66.605       18.2    30.5   12.4       67.6
## 54  50.65221  55.278     61.139       26.0    26.5   12.6       57.1
## 55  50.04934  57.096     72.573        9.6    14.0   19.6       66.5
## 56  52.43243  71.351     72.600        2.3    23.0    4.4       93.8
## 57  50.08490  23.059     52.315       34.2    23.9    9.3       27.3
## 58  50.72703  87.564     77.333        4.9    12.4    7.1       84.7
## 59  48.67937  59.152     74.315        4.3    17.0    7.0       89.3
## 60  48.02354  34.030     68.000       22.7    23.3   11.3       62.7
## 61  49.64388  55.325     69.156       12.7    26.4    6.0       73.3
## 62  49.43913  74.898     75.217        8.9    14.8   10.1       81.9
## 63  49.40868  70.473     68.277       15.3    21.3   28.4       54.7
## 64  50.42551  63.170     80.200        2.3    10.3    4.3       90.6
## 65  50.29813  92.418     80.700        1.9     9.6    4.1       85.8
## 66  51.37667  70.438     81.000        2.0     9.5    3.8       91.4
## 67  50.33983  55.674     72.708       10.2    14.7    8.7       80.0
## 68  51.15926  91.616     81.090        0.9     8.4    4.8       82.4
## 69  49.38968  90.979     72.628        9.5    19.2   10.9       78.4
## 70  51.51148  57.428     68.720        5.6    26.8    9.5       86.0
## 71  50.31602  27.030     63.539       19.6    13.4    9.6       27.1
## 72  39.54817 100.000     74.584        4.5    17.4   12.8       72.4
## 73  54.01017  68.142     69.900        2.0    21.9    5.4       91.8
## 74  49.70581  88.593     77.031        4.3    17.9    5.8       90.6
## 75  50.71690  28.153     49.837       34.9    26.6    8.3       32.3
## 76  49.76829  51.151     61.911       24.5    17.6   10.0       31.4
## 77  49.48379  80.102     69.672        6.4    20.1   20.1       71.9
## 78  53.79196  67.679     69.500        2.1    20.7    6.6       89.8
## 79  49.53926  90.981     80.100        1.4    10.0    6.7       88.4
## 80  50.12397  37.191     64.728       20.6    22.9   10.6       43.2
## 81  50.70152  16.937     60.155       22.4    16.4    8.6       31.7
## 82  48.57852  76.036     73.903        4.3    17.2    8.9       73.6
## 83  49.94100  42.356     57.718       32.7    24.6    8.9       30.5
## 84  49.82266  53.672     62.831       33.5    18.1    9.4       37.2
## 85  50.57711  40.793     71.300        9.2    22.6    4.8       88.7
## 86  51.08928  80.156     72.046        7.5    15.7   10.3       79.9
## 87  52.03556  42.629     67.439       11.9    24.9    5.7       90.1
## 88  50.66954  68.445     65.465        8.7    30.2   10.6       79.7
## 89  50.55901  66.813     74.205        1.7    20.6    3.5       95.0
## 90  50.40385  62.453     74.948       13.8    12.4    6.4       79.6
## 91  51.47519  35.988     56.293       27.8    18.4    7.8       26.9
## 92  51.80790  30.579     63.419       23.1    24.2    8.6       67.8
## 93  51.55369  50.032     60.020       15.6    21.3    9.8       40.9
## 94  54.53534  19.740     68.710       19.9    21.8    8.8       66.2
## 95  50.22094  91.490     80.000        2.1    11.2    5.2       89.6
## 96  50.83777  86.538     80.000        3.5    10.1    6.0       89.5
## 97  50.71271  58.522     70.551        9.4    14.2   12.7       76.4
## 98  49.77100  16.425     60.485       25.2    20.0   10.4       27.0
## 99  49.52463  82.248     80.900        1.5     9.2    5.6       87.0
## 100 34.01408  84.539     75.645        5.1    17.8   17.7       71.9
## 101 48.53807  36.666     66.041       42.0    24.7    7.3       57.8
## 102 49.90538  67.709     75.060        8.5    13.0    9.7       74.6
## 103 49.15161  61.585     72.041       10.7    17.5   12.0       74.4
## 104 50.33776  77.907     73.612        7.3    12.6   10.5       69.2
## 105 49.74166  46.907     66.971       13.5    26.8    7.5       67.3
## 106 51.53071  60.058     73.900        2.7    18.7    4.7       90.3
## 107 52.71196  65.211     78.100        2.1    11.1    4.2       85.6
## 108 24.49529  99.135     78.830        3.5    15.3   25.9       68.9
## 109 51.34374  53.998     71.700        3.4    21.4    3.6       92.2
## 110 50.86106  17.211     66.187       15.9    18.2   13.5       44.0
## 111 49.20878  52.198     70.075        9.7    23.2    5.6       81.0
## 112 42.44585  83.844     73.671        3.7    16.4   16.3       73.2
## 113 51.27733  47.192     65.242       20.6    18.1   12.2       42.1
## 114 51.00252  56.092     73.600        3.4    19.1    3.0       94.6
## 115 50.12127  42.055     53.049       32.8    30.5    8.9       33.2
## 116 47.65813 100.000     80.700        1.1     9.3    3.7       73.6
## 117 51.33833  53.726     73.800        2.8    17.2    6.0       89.2
## 118 50.24521  54.541     78.200        1.2    12.7    6.6       88.4
## 119 50.69415  66.355     60.162       10.7    26.2    9.1       51.3
## 120 49.91688  81.459     79.700        1.5     7.8   10.0       79.8
## 121 50.89664  80.321     80.500        1.7     9.9    3.5       91.4
## 122 51.96682  18.476     73.238        4.5    17.4    9.7       82.8
## 123 49.94578  87.431     80.600        1.5     9.1    4.9       89.9
## 124 50.42712  73.797     81.700        2.9     8.6    6.1       89.6
## 125 49.58384  27.134     68.493       15.0    25.3    7.6       69.2
## 126 51.26870  49.949     72.977        5.0    14.5   10.2       74.0
## 127 50.26805  41.702     59.644       24.9    23.6   10.7       37.6
## 128 50.43715  68.945     74.297       11.5    16.1    6.4       85.8
## 129 50.67781  75.143     74.149        5.5    16.1    6.2       89.4
## 130 50.77608  23.774     60.270       19.9    21.9   12.7       32.9
## 131 53.68775  69.352     67.020        5.2    24.7    5.0       91.0
## 132 30.63669  86.522     76.966        4.0    16.8   16.8       76.8
## 133 50.63527  83.398     79.400        2.6    10.9    3.5       88.8
## 134 50.52001  82.256     76.100        3.5    14.6    6.6       88.3
## 135 51.72154  95.334     73.805        4.5    16.7    7.5       84.9
## 136 50.13736  50.478     69.250       11.6    24.5    6.0       83.7
## 137 49.61166  36.642     64.413       27.0    30.6   14.7       56.6
## 138 50.49321  43.521     60.158       23.5    17.9   10.2       29.2
## 139 52.35675  32.209     59.105       20.9    19.3   12.3       33.0
##     Tuberculosis Diabetes ImmunSaramp HipTen.H HipTen.M BCG    Medicos
## 1          189.0      9.2          64     18.6     19.8   1 0.24009091
## 2           18.0      9.0          94     41.6     39.4   1 1.21237143
## 3           69.0      6.7          80     22.3     23.0   1 1.31202500
## 4          355.0      4.5          50     31.1     25.2   1 0.17300000
## 5           27.0      5.9          94     41.8     32.9   1 3.57165000
## 6           31.0      6.1          95     41.2     43.4   1 3.06122500
## 7            6.6      5.6          95     34.8     32.8   1 3.27402222
## 8            7.1      6.6          94     44.9     38.8   1 4.66801111
## 9           63.0      6.1          96     25.9     31.4   1 3.58736250
## 10         221.0      9.2          97     15.2     17.4   1 0.39047692
## 11          31.0      5.0          97     43.9     45.5   1 4.22154000
## 12           9.0      4.6          96     39.4     35.0   0 2.79814444
## 13          30.0     17.1          97     24.3     25.1   1 0.96785000
## 14          56.0      1.0          71     39.9     40.3   1 0.11874000
## 15         149.0     10.3          97     19.7     20.4   1 0.26431111
## 16         108.0      6.8          89     27.4     27.2   1 0.71612500
## 17          25.0      9.0          68     45.6     43.2   1 1.77503333
## 18         275.0      5.8          97     42.2     41.0   1 0.35774444
## 19          45.0     10.4          84     28.1     38.5   1 1.84654000
## 20          68.0     13.3          99     29.1     32.0   1 1.38302727
## 21          22.0      6.0          93     45.5     46.5   1 3.80887500
## 22          48.0      7.3          88      9.0     12.4   1 0.04237143
## 23         111.0      5.1          88     32.6     30.8   1 0.04885000
## 24         302.0      6.4          84     25.5     24.1   1 0.24971429
## 25         186.0      6.0          71     18.5     17.9   1 0.07534000
## 26           5.6      7.6          90     22.7     23.9   0 2.32944444
## 27         540.0      6.0          49     33.6     33.2   1 0.04973333
## 28          33.0      7.4          95     28.7     28.7   1 1.70129231
## 29          35.0     12.3          90     43.6     41.5   1 0.18323333
## 30         375.0      6.0          75     18.2     19.3   1 0.12880000
## 31           8.4      5.4          93     43.7     38.5   1 2.80697000
## 32           5.4      7.0          96     48.2     42.6   1 3.70292500
## 33           5.4      8.3          95     39.9     34.6   1 3.56958333
## 34         260.0      5.1          86     19.8     22.3   1 0.21512500
## 35          45.0      8.6          95     27.4     29.3   1 1.37733333
## 36          44.0      5.5          83     28.5     28.6   1 1.90360000
## 37          12.0     17.2          94     17.1     23.9   1 1.73048750
## 38          70.0      8.8          81     27.1     30.0   1 1.65428000
## 39         201.0      6.0          30     28.2     25.2   1 0.40000000
## 40          13.0      4.2          87     45.5     42.9   1 3.34070000
## 41         151.0      4.3          61     27.5     22.2   1 0.03620000
## 42          54.0     14.7          94     27.6     25.0   1 0.58042000
## 43           4.7      5.6          96     54.6     46.6   1 3.14394000
## 44           8.9      4.8          90     38.0     38.5   1 3.26508571
## 45         525.0      6.0          59     44.7     43.6   1 0.36110000
## 46         174.0      1.9          91     33.0     30.4   1 0.09858333
## 47          80.0      5.8          98     40.2     41.0   1 4.62520000
## 48           7.3     10.4          97     35.6     34.2   1 3.84022000
## 49         148.0      2.5          92     41.5     41.4   1 0.12015714
## 50           4.5      4.7          97     39.2     36.4   1 5.71011111
## 51          26.0     10.0          87     25.6     26.5   1 0.62945000
## 52         176.0      2.4          48     40.6     41.0   1 0.09196667
## 53          83.0     11.6          98     26.2     26.3   1 0.56823333
## 54         176.0      6.7          69     25.5     26.7   1 0.18585000
## 55          37.0      7.3          89     25.5     25.7   1 0.57160000
## 56           6.4      6.9          99     46.2     47.4   1 3.24861667
## 57         142.0      6.0          37     32.7     31.3   1 0.04200000
## 58          18.0      8.6          93     31.7     28.2   1 1.03645000
## 59          61.0      9.2          99     35.7     32.3   1 1.57320000
## 60         199.0     10.4          90     25.8     29.2   1 0.67390833
## 61         316.0      6.3          75     25.0     24.7   1 0.24655000
## 62          14.0      9.6          99     26.4     25.9   1 0.98516000
## 63          42.0      8.8          83     24.8     23.9   1 0.72533333
## 64           7.0      3.2          92     46.0     32.2   1 2.81283000
## 65           4.0      9.7          98     33.8     40.1   1 3.38105556
## 66           7.0      5.0          93     45.7     36.5   0 3.94927778
## 67           2.9     11.3          89     30.4     33.6   1 0.54227143
## 68          14.0      5.6          97     46.7     37.5   1 2.27806667
## 69           5.0     12.7          92     29.3     26.3   1 2.24923636
## 70          68.0      6.1          99     33.9     33.1   1 3.60587000
## 71         292.0      3.1          89     34.7     36.8   1 0.18122857
## 72          23.0     12.2          99     24.5     26.5   1 2.16873636
## 73          29.0      5.0          98     44.7     36.7   1 3.38225000
## 74          11.0     11.2          82     30.6     29.6   0 2.55344444
## 75         611.0      4.5          90     17.5     35.0   1 0.06760000
## 76         308.0      2.4          91     39.9     40.3   1 0.02466667
## 77          40.0     10.2          97     23.4     23.6   1 1.98748333
## 78          44.0      3.8          92     43.5     40.5   1 4.08613000
## 79           8.0      5.0          99     48.5     37.1   1 2.85313000
## 80         233.0      4.5          62     33.1     32.2   1 0.17360000
## 81         181.0      4.5          87     33.4     32.6   1 0.01767500
## 82          92.0     16.7          96     26.5     25.2   1 1.24358000
## 83          53.0      2.4          70     32.6     32.1   1 0.09575714
## 84          93.0      7.1          78     26.3     29.5   1 0.14066667
## 85          13.0     22.0          99     49.0     48.4   1 1.45775556
## 86          23.0     13.5          97     29.3     28.5   1 2.03156667
## 87          86.0      5.7          93     43.3     42.2   1 2.62560000
## 88         428.0      4.7          99     37.3     30.6   1 2.96358889
## 89          15.0      9.0          58     43.4     41.9   1 2.08927778
## 90          99.0      7.0          99     33.5     33.0   1 0.63655000
## 91         551.0      3.3          85     36.1     31.5   1 0.04688750
## 92         338.0      3.9          93     24.7     25.5   1 0.53712000
## 93         524.0      4.5          82     43.2     41.9   1 0.37305000
## 94         151.0      7.2          91     21.6     22.1   1 0.57297500
## 95           5.3      5.4          93     39.1     33.8   0 3.40828889
## 96           7.3      6.2          92     36.4     30.9   1 2.70726000
## 97          41.0     11.4          99     25.4     26.2   1 0.74390000
## 98          87.0      2.4          77     33.8     32.4   1 0.03340000
## 99           4.1      5.3          96     38.2     33.0   1 4.21626667
## 100          5.9     10.1          99     22.2     26.2   1 1.95183077
## 101        265.0     19.9          76     20.1     21.1   1 0.84727500
## 102         52.0      7.7          98     27.7     24.6   1 1.43397000
## 103         43.0      9.6          93     27.8     27.2   1 0.96393333
## 104        123.0      6.6          85     16.2     17.8   1 1.18280000
## 105        554.0      7.1          67     28.0     23.5   1 1.25186667
## 106         16.0      6.1          93     40.9     35.0   1 2.19399000
## 107         24.0      9.8          99     38.6     35.7   1 3.79097273
## 108         31.0     15.6          99     26.8     36.2   1 2.47302500
## 109         68.0      6.9          90     39.1     46.7   1 2.40146667
## 110         59.0      5.1          99     32.2     30.4   1 0.09168889
## 111          6.3     11.6          99     27.9     30.7   1 0.65870000
## 112         10.0     15.8          98     25.6     27.0   1 2.25626250
## 113        118.0      2.4          82     42.3     40.4   1 0.14020000
## 114         17.0      9.0          92     43.5     41.6   1 2.42901111
## 115        298.0      2.4          80     39.8     39.7   1 0.02050000
## 116         47.0      5.5          95     25.7     22.1   1 1.76859091
## 117          5.8      6.5          96     39.5     33.7   1 3.11790000
## 118          5.3      5.9          93     42.5     37.2   1 2.59476000
## 119        520.0     12.7          70     47.0     45.9   1 0.76988750
## 120         66.0      6.9          98     28.6     23.5   1 2.05984615
## 121          9.4      6.9          97     44.0     37.1   1 4.15920000
## 122         64.0     10.7          99     23.0     25.8   1 0.77147000
## 123          5.5      4.8          97     41.8     37.0   0 4.03310000
## 124          6.4      5.7          96     38.6     31.7   1 4.00455000
## 125         84.0      6.1          98     26.3     25.4   1 1.78443333
## 126        153.0      7.0          96     23.4     23.8   1 0.40473333
## 127         36.0      2.4          85     39.5     40.1   1 0.08897500
## 128         35.0      8.5          96     20.9     25.2   1 1.16097143
## 129         16.0     11.1          96     38.3     35.1   1 1.65210000
## 130        200.0      2.5          86     24.6     32.7   1 0.10457500
## 131         80.0      6.1          91     47.7     50.6   1 3.35196667
## 132          1.0     16.3          99     17.9     19.2   1 1.70489167
## 133          8.0      3.9          92     31.7     29.9   1 2.75287500
## 134          3.0     10.8          92     31.1     31.8   0 2.52747500
## 135         33.0      7.3          97     37.5     38.8   1 4.16766000
## 136         70.0      6.5          96     27.1     27.7   1 2.50781250
## 137         48.0      5.4          64      9.6     12.4   1 0.30986667
## 138        346.0      4.5          94     27.2     27.4   1 0.08791250
## 139        210.0      1.8          88     33.2     32.0   1 0.06561250
##          Camas         PBI TempMarzo l10muertes.permil muertes.permil
## 1    0.4363636   1.8351696      7.60        0.85166698     6.10668360
## 2    2.9375000  11.3351950      6.04        1.09735434    11.51279525
## 3    1.9000000  14.1967389     17.91        1.19736588    14.75309441
## 4    0.8000000   6.7205961     22.78        0.05301271     0.12982898
## 5    4.6000000  20.0684923     17.51        1.08768715    11.23734344
## 6    4.0200000   8.3491802     -0.57        1.53403057    33.20035125
## 7    3.8783333  45.7525548     25.37        0.70937665     4.12125797
## 8    7.7000000  48.9687140      1.42        1.86867832    72.90576495
## 9    6.4666667  17.0906963      4.97        0.80830014     5.43132025
## 10   0.5750000   3.3061083     25.42        0.64062316     3.37142634
## 11  11.2000000  18.1721809     -0.69        1.37219412    22.56102177
## 12   6.5500000  45.2631622      5.23        2.91424609   819.81651659
## 13   1.1600000   8.0937796     24.45        0.79385771     5.22096426
## 14   0.5000000   2.0675705     30.14        0.10078712     0.26120918
## 15   1.7333333   8.3417246      5.68        0.00000000     0.00000000
## 16   1.1000000   6.5317860     22.07        1.40930364    24.66277617
## 17   3.3666667  11.6971771      4.01        1.66678153    45.42816649
## 18   2.0000000  16.1336867     24.30        0.15945618     0.44363092
## 19   2.3285714  15.5847506     25.49        2.09062497   122.20404597
## 20   2.7542857  80.8004129     25.92        0.75300190     4.66241765
## 21   6.4888889  17.9465777      4.70        1.29960529    18.93449746
## 22   0.6500000   1.6709928     30.63        0.56624131     2.68333575
## 23   1.1333333   0.7568378     20.43        0.03722023     0.08948243
## 24   0.7600000   3.3333517     27.93        0.00000000     0.00000000
## 25   1.4000000   3.2930885     26.27        0.90413575     7.01928682
## 26   3.0714286  44.2264907    -18.72        2.26374838   182.54745910
## 27   1.1000000   0.8532664     26.96        0.08432564     0.21429902
## 28   1.3166667  13.2116325     25.14        1.23486238    16.17364085
## 29   2.2000000   2.6420144     25.20        0.53185125     2.40291618
## 30   1.6000000   5.6636489     25.60        0.66491803     3.62293762
## 31   5.6354545  22.9922117      5.83        1.40989932    24.69799971
## 32   7.0100000  32.7718057      2.80        1.48902061    29.83334267
## 33   3.4111111  48.5249925      2.24        1.99324531    97.45670766
## 34   1.4571429   2.7442687     25.75        1.29603122    18.77111751
## 35   1.4375000  13.9050879     22.88        1.65899033    44.60267625
## 36   1.5285714  10.8759023     21.93        2.28487232   191.69583028
## 37   1.5100000  10.8110341     17.83        0.96804820     8.29069493
## 38   0.9900000   7.2348467     25.70        0.84966889     6.07406245
## 39   2.0666667  30.5908474     25.04        1.00721356     9.16748537
## 40   5.3600000  28.8340001     -2.35        1.70728513    49.96653756
## 41   1.7500000   1.5126247     23.48        0.02322475     0.05493270
## 42   2.1475000   8.8754441     25.00        0.00000000     0.00000000
## 43   5.9600000  42.8552430     -6.09        1.76134842    56.72293654
## 44   6.9222222  40.3515680      6.37        2.63132934   426.88724438
## 45   3.2000000  17.0778163     26.14        0.88115819     6.60603272
## 46   1.0000000   2.4051919     27.32        0.15793307     0.43857687
## 47   3.1222222   9.5834608      0.72        0.62493108     3.21629590
## 48   8.2555556  46.5762068      3.87        2.01040779   101.42542822
## 49   0.9000000   3.9219764     29.52        0.33086008     1.14220031
## 50   4.6555556  27.2065489      8.19        1.23366924    16.12652442
## 51   0.6250000   7.5159291     22.94        0.69394933     3.94253020
## 52   0.3000000   2.0022876     27.63        0.43000974     1.69159514
## 53   2.1600000   7.1987245     25.65        1.17956888    14.12059502
## 54   1.0000000   1.7058976     23.47        0.60817085     3.05668093
## 55   0.7285714   4.4336960     23.38        1.32704478    20.23463414
## 56   7.3000000  25.7573736      5.44        1.72513328    52.10473974
## 57   0.4000000   2.0073216     26.55        0.71053771     4.13496767
## 58   2.1714286  22.2523960     10.87        1.66184336    44.90324179
## 59   3.4066667  13.5313781      0.49        0.63650295     3.33015014
## 60   0.8000000   5.8366566     23.45        0.63846947     3.34980183
## 61   0.9000000  10.5772045     25.79        0.81312576     5.50317977
## 62   1.4400000  18.4501839     11.33        1.97066822    92.46913357
## 63   1.3100000  15.8895142     15.12        0.74455172     4.55330752
## 64   3.9555556  59.3055780      6.00        2.52768884   336.04573683
## 65   3.5333333  34.5710854     14.96        1.51362517    31.63060852
## 66   3.6875000  37.7630816      6.52        2.73899149   547.26622303
## 67   1.7571429   8.5085469     23.44        0.60923050     3.06659102
## 68  13.7960000  39.1530060      2.52        0.89103814     6.78104879
## 69   1.7818182   9.2014965     13.08        0.27966158     0.90397650
## 70   7.4111111  24.1030338     -3.96        0.48064752     2.02445775
## 71   1.4000000   2.8839337     26.10        0.31600904     1.07018445
## 72   1.9800000  75.8307296     19.23        1.63646809    42.29802512
## 73   6.8111111  23.8146826     -1.37        1.11188355    11.93848875
## 74   3.3800000  13.2450688     10.39        0.68089875     3.79621619
## 75   1.3000000   2.9043623     15.13        0.00000000     0.00000000
## 76   0.7500000   1.2563111     26.44        0.81973136     5.60284890
## 77   3.6700000  19.3396159     17.76        0.20382969     0.59893088
## 78   7.1666667  27.8087523     -0.47        1.39199086    23.65987425
## 79   5.3727273 100.2191161      4.70        2.26007623   181.00202722
## 80   0.2500000   1.7044431     23.99        0.03187466     0.07615460
## 81   1.2000000   1.1836484     23.25        0.08652600     0.22046688
## 82   1.8312500  25.8714034     25.19        0.66721788     3.64748370
## 83   0.3333333   2.0131236     27.34        0.66924316     3.66920733
## 84   0.4000000   3.8172107     25.29        0.66592067     3.63362273
## 85   3.2333333  19.3987280     25.48        0.94954834     7.90324531
## 86   1.5888889  17.8555895     18.44        1.83964772    68.12700147
## 87   6.1666667   5.8933274      2.93        1.89361039    77.27271317
## 88   6.3266667  11.1277975     -8.80        0.00000000     0.00000000
## 89   4.0125000  16.2734892      2.53        1.18924972    14.46143216
## 90   0.9800000   7.4795258     13.09        0.81997625     5.60657321
## 91   0.7666667   1.2616857     25.48        0.01447977     0.03390295
## 92   0.7500000   5.0214146     22.74        0.04599322     0.11171438
## 93   3.0000000  10.2230967     22.67        0.00000000     0.00000000
## 94   2.6500000   2.4752041     10.02        0.05782208     0.14241022
## 95   4.6000000  49.9843155      4.95        2.53367393   340.72277916
## 96   2.5500000  36.4995043     13.60        0.74060910     4.50312148
## 97   0.9222222   4.8837325     24.95        0.80708405     5.41333688
## 98   0.3000000   0.9278714     26.01        0.58564967     2.85167528
## 99   4.2555556  62.6503184     -5.49        1.65533067    44.22001168
## 100  1.8600000  42.4790524     23.40        0.94784219     7.86833705
## 101  0.7200000   4.6501098     16.01        0.84119511     5.93737399
## 102  2.2888889  20.8839055     25.44        1.88318015    75.41526879
## 103  1.2857143  11.4029323     25.84        0.41184731     1.58135246
## 104  1.4875000  12.3515166     19.99        2.09868025   124.51055442
## 105  0.5833333   7.0082665     25.13        0.97663292     8.47617167
## 106  6.5222222  25.9904310      2.65        1.44821011    27.06791213
## 107  3.4222222  29.3390041     11.33        2.12347272   131.88400976
## 108  1.6400000 123.2139364     21.78        1.07132447    10.78486107
## 109  6.4555556  21.6182719      3.40        1.80343313    62.59648794
## 110  1.6000000   1.7889788     19.25        0.00000000     0.00000000
## 111  3.3000000  10.9317631     25.90        0.00000000     0.00000000
## 112  2.2500000  51.5878305     20.60        1.13389947    12.61129580
## 113  0.2000000   3.1310271     28.47        0.53906244     2.45989116
## 114  5.6125000  14.9080484      4.91        1.54868038    34.37369129
## 115  0.4000000   1.4959394     27.65        0.83772947     5.88223453
## 116  2.7457143  86.0684237     28.62        0.70577580     4.07897173
## 117  6.3400000  29.0915206      2.40        0.78819906     5.14043390
## 118  4.6555556  31.7402860      3.38        1.72227622    51.75652955
## 119  2.8000000  12.8666891     21.10        1.02339821     9.55354121
## 120 10.6080000  34.6370853      3.66        0.79306494     5.20961879
## 121  3.1888889  34.5888453      8.60        2.76446761   580.39007101
## 122  3.5500000  11.1185327     27.03        0.16478915     0.46146747
## 123  2.7666667  47.6285919     -4.98        2.61847597   414.40906201
## 124  5.1222222  61.3146089      0.09        2.28867018   193.38832670
## 125  5.2666667   2.7293337     -3.08        0.78207686     5.05448015
## 126  2.1000000  15.8571484     27.38        0.26030714     0.82098822
## 127  0.8000000   1.4956789     29.31        0.42289247     1.64784448
## 128  2.0545455  11.2660561     14.21        0.71183934     4.15038075
## 129  2.4555556  23.5212137      4.77        1.73570816    53.41368734
## 130  0.7500000   1.8103287     23.68        0.00000000     0.00000000
## 131  8.9222222   8.4417522      1.18        1.18842716    14.43217590
## 132  1.6777778  65.5180899     22.61        1.43897112    26.47711407
## 133  3.2111111  41.1611271      4.66        2.75158819   563.40154117
## 134  3.0333333  55.0581658      0.06        2.48855760   307.00488362
## 135  2.5250000  20.4797528     21.26        0.86794498     6.37810755
## 136  4.5888889   6.8361061      5.38        0.15375896     0.42481657
## 137  0.6900000   3.5314239     20.82        0.45632576     1.85973480
## 138  1.9500000   3.8025013     22.90        0.14718638     0.40341585
## 139  2.3500000   2.5606953     22.92        0.10620013     0.27702715

El primer paso a la hora de establecer un modelo lineal múltiple es estudiar la relación que existe entre variables. Esta información es crítica a la hora de identificar cuáles pueden ser los mejores predictores para el modelo, y para detectar colinealidad entre predictores.

En primer lugar, se ajusta un modelo de regresión lineal (OLS) incluyendo todas las variables como predictores.

ajustels<-lm(l10muertes.permil~.,data = df_covid_total )
summary(ajustels)
## 
## Call:
## lm(formula = l10muertes.permil ~ ., data = df_covid_total)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73722 -0.23692 -0.00353  0.23116  0.90465 
## 
## Coefficients: (1 not defined because of singularities)
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -2.9869140  2.2438896  -1.331  0.18580    
## Hombres80      -0.1855343  0.1279285  -1.450  0.14972    
## Mujeres80       0.0235768  0.0857817   0.275  0.78393    
## Pobla80                NA         NA      NA       NA    
## Pobla65         0.0648962  0.0352450   1.841  0.06818 .  
## PoblaMid        0.0284269  0.0148852   1.910  0.05868 .  
## PoblaData      -0.0100806  0.0205972  -0.489  0.62549    
## PoblaDens      -0.0115589  0.0060235  -1.919  0.05749 .  
## Mujeres         0.0226914  0.0200892   1.130  0.26105    
## Urbano          0.0066679  0.0025300   2.636  0.00957 ** 
## ExpectVida      0.0210514  0.0209453   1.005  0.31699    
## NeontlMort      0.0054847  0.0089112   0.615  0.53946    
## DisMort        -0.0012891  0.0146930  -0.088  0.93024    
## Lesion         -0.0020957  0.0128853  -0.163  0.87109    
## EnfNoTrans     -0.0116425  0.0088629  -1.314  0.19161    
## Tuberculosis   -0.0009200  0.0003822  -2.407  0.01768 *  
## Diabetes        0.0185090  0.0124622   1.485  0.14025    
## ImmunSaramp    -0.0088272  0.0034935  -2.527  0.01288 *  
## HipTen.H       -0.0114184  0.0100937  -1.131  0.26033    
## HipTen.M        0.0141302  0.0110152   1.283  0.20217    
## BCG             0.4210976  0.1976027   2.131  0.03524 *  
## Medicos         0.0513170  0.0590776   0.869  0.38687    
## Camas          -0.0632265  0.0257027  -2.460  0.01540 *  
## PBI             0.0014607  0.0032764   0.446  0.65657    
## TempMarzo      -0.0130534  0.0059009  -2.212  0.02895 *  
## muertes.permil  0.0033422  0.0003986   8.386 1.52e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3843 on 114 degrees of freedom
## Multiple R-squared:  0.7871, Adjusted R-squared:  0.7422 
## F-statistic: 17.56 on 24 and 114 DF,  p-value: < 2.2e-16

El valor R2ajustadoRajustado2 obtenido es muy alto (0.7871) lo que indica que el modelo es capaz de predecir con gran exactitud las muertes por covid de las observaciones con las que se ha entrenado. El hecho de que el modelo en conjunto sea significativo (p-value: < 2.2e-16), pero que muy pocos de los predictores lo sean a nivel individual, es indicativo de una posible redundancia entre los predictores (colinealidad).

Analizamos la matriz de correlacion

round(cor(df_covid_total),2)
##                   Hombres80 Mujeres80 Pobla80 Pobla65 PoblaMid PoblaData
## Hombres80              1.00      0.97    0.99    0.96     0.28     -0.02
## Mujeres80              0.97      1.00    1.00    0.98     0.27     -0.04
## Pobla80                0.99      1.00    1.00    0.98     0.28     -0.04
## Pobla65                0.96      0.98    0.98    1.00     0.32      0.00
## PoblaMid               0.28      0.27    0.28    0.32     1.00      0.11
## PoblaData             -0.02     -0.04   -0.04    0.00     0.11      1.00
## PoblaDens              0.01      0.01    0.01    0.04     0.19      0.02
## Mujeres                0.28      0.30    0.30    0.32    -0.43     -0.04
## Urbano                 0.48      0.46    0.47    0.46     0.53     -0.07
## ExpectVida             0.72      0.65    0.68    0.70     0.66      0.04
## NeontlMort            -0.69     -0.69   -0.69   -0.72    -0.68      0.02
## DisMort               -0.57     -0.49   -0.53   -0.51    -0.23      0.04
## Lesion                -0.60     -0.61   -0.61   -0.65    -0.08     -0.01
## EnfNoTrans             0.71      0.70    0.71    0.76     0.72      0.05
## Tuberculosis          -0.50     -0.47   -0.48   -0.49    -0.37      0.04
## Diabetes              -0.08     -0.14   -0.12   -0.09     0.50      0.12
## ImmunSaramp            0.35      0.35    0.35    0.36     0.53      0.04
## HipTen.H               0.52      0.58    0.56    0.58    -0.01     -0.08
## HipTen.M               0.39      0.47    0.45    0.47     0.01     -0.07
## BCG                   -0.32     -0.27   -0.29   -0.28    -0.05     -0.03
## Medicos                0.78      0.81    0.81    0.80     0.46     -0.06
## Camas                  0.65      0.74    0.71    0.73     0.33     -0.05
## PBI                    0.46      0.44    0.45    0.44     0.59     -0.06
## TempMarzo             -0.65     -0.69   -0.68   -0.71    -0.31     -0.04
## l10muertes.permil      0.68      0.65    0.67    0.66     0.36     -0.02
## muertes.permil         0.55      0.49    0.52    0.47     0.10      0.00
##                   PoblaDens Mujeres Urbano ExpectVida NeontlMort DisMort Lesion
## Hombres80              0.01    0.28   0.48       0.72      -0.69   -0.57  -0.60
## Mujeres80              0.01    0.30   0.46       0.65      -0.69   -0.49  -0.61
## Pobla80                0.01    0.30   0.47       0.68      -0.69   -0.53  -0.61
## Pobla65                0.04    0.32   0.46       0.70      -0.72   -0.51  -0.65
## PoblaMid               0.19   -0.43   0.53       0.66      -0.68   -0.23  -0.08
## PoblaData              0.02   -0.04  -0.07       0.04       0.02    0.04  -0.01
## PoblaDens              1.00   -0.06   0.13       0.15      -0.10   -0.15  -0.11
## Mujeres               -0.06    1.00  -0.25      -0.12       0.02    0.04  -0.48
## Urbano                 0.13   -0.25   1.00       0.66      -0.63   -0.57  -0.14
## ExpectVida             0.15   -0.12   0.66       1.00      -0.87   -0.70  -0.31
## NeontlMort            -0.10    0.02  -0.63      -0.87       1.00    0.54   0.34
## DisMort               -0.15    0.04  -0.57      -0.70       0.54    1.00   0.19
## Lesion                -0.11   -0.48  -0.14      -0.31       0.34    0.19   1.00
## EnfNoTrans             0.03    0.07   0.55       0.85      -0.88   -0.34  -0.47
## Tuberculosis          -0.05    0.07  -0.37      -0.68       0.60    0.40   0.13
## Diabetes               0.01   -0.39   0.16       0.27      -0.21    0.08   0.16
## ImmunSaramp            0.08   -0.05   0.29       0.58      -0.61   -0.30  -0.15
## HipTen.H              -0.08    0.32   0.22       0.21      -0.34   -0.22  -0.52
## HipTen.M              -0.12    0.25   0.15       0.08      -0.25   -0.07  -0.44
## BCG                   -0.02   -0.03  -0.26      -0.28       0.21    0.26   0.18
## Medicos                0.00    0.08   0.60       0.71      -0.77   -0.40  -0.48
## Camas                  0.00    0.22   0.37       0.47      -0.61   -0.23  -0.47
## PBI                    0.26   -0.48   0.66       0.68      -0.61   -0.56  -0.11
## TempMarzo              0.12   -0.21  -0.32      -0.53       0.58    0.22   0.48
## l10muertes.permil     -0.04    0.00   0.57       0.65      -0.57   -0.53  -0.33
## muertes.permil        -0.01    0.07   0.35       0.44      -0.34   -0.45  -0.27
##                   EnfNoTrans Tuberculosis Diabetes ImmunSaramp HipTen.H
## Hombres80               0.71        -0.50    -0.08        0.35     0.52
## Mujeres80               0.70        -0.47    -0.14        0.35     0.58
## Pobla80                 0.71        -0.48    -0.12        0.35     0.56
## Pobla65                 0.76        -0.49    -0.09        0.36     0.58
## PoblaMid                0.72        -0.37     0.50        0.53    -0.01
## PoblaData               0.05         0.04     0.12        0.04    -0.08
## PoblaDens               0.03        -0.05     0.01        0.08    -0.08
## Mujeres                 0.07         0.07    -0.39       -0.05     0.32
## Urbano                  0.55        -0.37     0.16        0.29     0.22
## ExpectVida              0.85        -0.68     0.27        0.58     0.21
## NeontlMort             -0.88         0.60    -0.21       -0.61    -0.34
## DisMort                -0.34         0.40     0.08       -0.30    -0.22
## Lesion                 -0.47         0.13     0.16       -0.15    -0.52
## EnfNoTrans              1.00        -0.63     0.32        0.56     0.30
## Tuberculosis           -0.63         1.00    -0.24       -0.44    -0.14
## Diabetes                0.32        -0.24     1.00        0.23    -0.28
## ImmunSaramp             0.56        -0.44     0.23        1.00     0.09
## HipTen.H                0.30        -0.14    -0.28        0.09     1.00
## HipTen.M                0.24        -0.08    -0.21        0.09     0.90
## BCG                    -0.21         0.17     0.02       -0.06    -0.07
## Medicos                 0.79        -0.52     0.03        0.46     0.48
## Camas                   0.61        -0.33    -0.09        0.37     0.53
## PBI                     0.49        -0.41     0.19        0.35     0.21
## TempMarzo              -0.68         0.37     0.08       -0.33    -0.43
## l10muertes.permil       0.59        -0.48     0.06        0.23     0.32
## muertes.permil          0.32        -0.26    -0.13        0.16     0.23
##                   HipTen.M   BCG Medicos Camas   PBI TempMarzo
## Hombres80             0.39 -0.32    0.78  0.65  0.46     -0.65
## Mujeres80             0.47 -0.27    0.81  0.74  0.44     -0.69
## Pobla80               0.45 -0.29    0.81  0.71  0.45     -0.68
## Pobla65               0.47 -0.28    0.80  0.73  0.44     -0.71
## PoblaMid              0.01 -0.05    0.46  0.33  0.59     -0.31
## PoblaData            -0.07 -0.03   -0.06 -0.05 -0.06     -0.04
## PoblaDens            -0.12 -0.02    0.00  0.00  0.26      0.12
## Mujeres               0.25 -0.03    0.08  0.22 -0.48     -0.21
## Urbano                0.15 -0.26    0.60  0.37  0.66     -0.32
## ExpectVida            0.08 -0.28    0.71  0.47  0.68     -0.53
## NeontlMort           -0.25  0.21   -0.77 -0.61 -0.61      0.58
## DisMort              -0.07  0.26   -0.40 -0.23 -0.56      0.22
## Lesion               -0.44  0.18   -0.48 -0.47 -0.11      0.48
## EnfNoTrans            0.24 -0.21    0.79  0.61  0.49     -0.68
## Tuberculosis         -0.08  0.17   -0.52 -0.33 -0.41      0.37
## Diabetes             -0.21  0.02    0.03 -0.09  0.19      0.08
## ImmunSaramp           0.09 -0.06    0.46  0.37  0.35     -0.33
## HipTen.H              0.90 -0.07    0.48  0.53  0.21     -0.43
## HipTen.M              1.00 -0.02    0.40  0.45  0.11     -0.32
## BCG                  -0.02  1.00   -0.23 -0.08 -0.23      0.31
## Medicos               0.40 -0.23    1.00  0.70  0.56     -0.73
## Camas                 0.45 -0.08    0.70  1.00  0.37     -0.71
## PBI                   0.11 -0.23    0.56  0.37  1.00     -0.35
## TempMarzo            -0.32  0.31   -0.73 -0.71 -0.35      1.00
## l10muertes.permil     0.24 -0.39    0.62  0.36  0.54     -0.51
## muertes.permil        0.12 -0.59    0.39  0.20  0.38     -0.34
##                   l10muertes.permil muertes.permil
## Hombres80                      0.68           0.55
## Mujeres80                      0.65           0.49
## Pobla80                        0.67           0.52
## Pobla65                        0.66           0.47
## PoblaMid                       0.36           0.10
## PoblaData                     -0.02           0.00
## PoblaDens                     -0.04          -0.01
## Mujeres                        0.00           0.07
## Urbano                         0.57           0.35
## ExpectVida                     0.65           0.44
## NeontlMort                    -0.57          -0.34
## DisMort                       -0.53          -0.45
## Lesion                        -0.33          -0.27
## EnfNoTrans                     0.59           0.32
## Tuberculosis                  -0.48          -0.26
## Diabetes                       0.06          -0.13
## ImmunSaramp                    0.23           0.16
## HipTen.H                       0.32           0.23
## HipTen.M                       0.24           0.12
## BCG                           -0.39          -0.59
## Medicos                        0.62           0.39
## Camas                          0.36           0.20
## PBI                            0.54           0.38
## TempMarzo                     -0.51          -0.34
## l10muertes.permil              1.00           0.73
## muertes.permil                 0.73           1.00

Podemos observar varios casos donde la correlación es del tipo alta y casi perfecta

ggcorr(df_covid_total, method = c("everything", "pearson")) 

Conociendo esto decidimos eliminar las siguientes variables:

  • Nos quedamos con Pobla80 que representa la media entre las mujeres de 80 y hombres de 80

  • Dado que entre el porcentaje de poblacion de 80 y 65 también existe una colinealidad perfecta, decidimos quedarnos con el porcentaje de 80 años

  • El de porcentaje de hipertención obtamos quedarnos unicamente con el de mujeres

  • Entre la expectativa de vida, las enfermedades no transmisoras y el neontmort, vamos a mantener la expectativa de vida

Las variables seleccionadas entonces son:

df_covid_total = data.frame( Pobla80,PoblaMid, PoblaData, PoblaDens, Mujeres, Urbano,DisMort, Lesion,Tuberculosis,Diabetes, ImmunSaramp,HipTen.M, BCG,Medicos,Camas,PBI,TempMarzo,l10muertes.permil)

Volvemos a correr un modelo considerando estas variables

ajustels<-lm(l10muertes.permil~.,data = df_covid_total )
summary(ajustels)
## 
## Call:
## lm(formula = l10muertes.permil ~ ., data = df_covid_total)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.03352 -0.32453  0.00078  0.28469  1.18822 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2074581  1.4481831  -0.143 0.886328    
## Pobla80       0.1678347  0.0491913   3.412 0.000878 ***
## PoblaMid      0.0179866  0.0113805   1.580 0.116610    
## PoblaData     0.0015105  0.0252924   0.060 0.952477    
## PoblaDens    -0.0135871  0.0069675  -1.950 0.053481 .  
## Mujeres       0.0189388  0.0206484   0.917 0.360862    
## Urbano        0.0059219  0.0030802   1.923 0.056884 .  
## DisMort      -0.0104358  0.0117804  -0.886 0.377449    
## Lesion        0.0007857  0.0140986   0.056 0.955648    
## Tuberculosis -0.0007458  0.0003891  -1.917 0.057647 .  
## Diabetes      0.0012350  0.0142983   0.086 0.931310    
## ImmunSaramp  -0.0080585  0.0040730  -1.979 0.050142 .  
## HipTen.M      0.0035230  0.0064671   0.545 0.586924    
## BCG          -0.3865863  0.2136311  -1.810 0.072840 .  
## Medicos      -0.0064583  0.0677158  -0.095 0.924176    
## Camas        -0.0913157  0.0294257  -3.103 0.002383 ** 
## PBI           0.0066034  0.0038957   1.695 0.092639 .  
## TempMarzo    -0.0113363  0.0066844  -1.696 0.092470 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4786 on 121 degrees of freedom
## Multiple R-squared:  0.6495, Adjusted R-squared:  0.6002 
## F-statistic: 13.19 on 17 and 121 DF,  p-value: < 2.2e-16
XX<-model.matrix(ajustels)   #matriz de disenio
det(solve(t(XX)%*%XX))#determinante de (X'X)^-1 
## [1] 5.933772e-60

Análisis de inflación de varianza

vif(ajustels)
##      Pobla80     PoblaMid    PoblaData    PoblaDens      Mujeres       Urbano 
##     5.808401     3.131862     1.085931     1.367075     3.199217     2.739142 
##      DisMort       Lesion Tuberculosis     Diabetes  ImmunSaramp     HipTen.M 
##     2.619971     2.241925     1.782519     1.748519     1.746224     1.551978 
##          BCG      Medicos        Camas          PBI    TempMarzo 
##     1.324575     5.538386     3.183954     4.258259     3.316220

Ninguna variable super el 10 por lo que no estamos en presencia de un problema de colinealidad

dwt(ajustels, alternative = "two.sided")
##  lag Autocorrelation D-W Statistic p-value
##    1      0.07098641      1.853144   0.368
##  Alternative hypothesis: rho != 0

Seleccion de predictores

A la hora de seleccionar los predictores que deben formar parte del modelo se pueden seguir varios métodos para este ejercicio emplearemos el método paso a paso (stepwise), el cualemplea criterios matemáticos para decidir qué predictores contribuyen significativamente al modelo y en qué orden se introducen. Dentro de este método se diferencias tres estrategias: Dirección forward,Dirección backward o Doble o mixto.

El método paso a paso requiere de algún criterio matemático para determinar si el modelo mejora o empeora con cada incorporación o extracción. Existen varios parámetros empelados, de entre los que destacan el \(Cp\) \(AIC\), \(BIC\) y $R^2ajustado$, cada uno de ellos con ventajas e inconvenientes. El método Akaike(AIC) tiende a ser más restrictivo e introducir menos predictores que el R2-ajustado. Para un mismo set de datos, no todos los métodos tienen porque concluir en un mismo modelo.

En este caso se van a emplear la estrategia de stepwise mixto. El valor matemático empleado para determinar la calidad del modelo va a ser Akaike(AIC). Vamos a utilizar como variables de respuesta tanto la logaritmica como la de muertes por mil.

Modelo considerando l10muertes.permil

df_covid_l10muertes = data.frame(Pobla80,PoblaMid, PoblaData, PoblaDens, Mujeres, Urbano,DisMort, Lesion,Tuberculosis,Diabetes, ImmunSaramp,HipTen.M, BCG,Medicos,Camas,PBI,TempMarzo,l10muertes.permil)

df_covid_l10muertes
##       Pobla80 PoblaMid   PoblaData   PoblaDens  Mujeres  Urbano DisMort Lesion
## 1   0.2771541 54.32490  0.37172386  0.56937760 48.63585  25.495    29.8   19.5
## 2   2.7410163 68.58239  0.02866376  1.04612263 49.06309  60.319    17.0    4.0
## 3   1.2709846 63.48882  0.42228429  0.17730075 49.48427  72.629    14.2    9.5
## 4   0.2723907 50.97470  0.30809762  0.24713052 50.53046  65.514    16.5    9.2
## 5   2.6111754 64.12128  0.44494502  0.16258510 51.23735  91.870    15.8    6.5
## 6   3.1239347 68.11276  0.02951776  1.03680225 52.95658  63.149    22.3    3.9
## 7   4.0436339 65.15291  0.24992369  0.03249129 50.19962  86.012     9.1    5.9
## 8   5.2369767 66.70049  0.08847037  1.07206927 50.82943  58.297    11.4    5.2
## 9   1.3597424 70.43525  0.09942334  1.20265320 50.11575  55.680    22.2    4.6
## 10  0.9964088 67.13559  1.61356039 12.39579312 49.38730  36.632    21.6    7.5
## 11  3.8045048 68.28891  0.09485386  0.46728800 53.45605  78.595    23.7    7.0
## 12  5.6948380 64.15583  0.11422068  3.77214927 50.59332  98.001    11.4    6.4
## 13  1.0395255 64.98378  0.00383071  0.16793994 50.19252  45.724    22.1   13.2
## 14  0.4365651 54.29871  0.11485048  1.01853920 50.09820  47.312    19.6   10.2
## 15  1.2095158 68.22563  0.00754394  0.19777528 47.00264  40.895    23.3   10.5
## 16  1.6248620 61.73450  0.11353142  0.10480146 49.78340  69.425    17.2   13.1
## 17  3.3861895 68.76346  0.03323929  0.64920488 51.01054  48.245    17.8    3.7
## 18  0.4939442 61.66318  0.02254126  0.03977425 51.73076  69.446    20.3    8.3
## 19  1.8060280 69.74309  2.09469333  0.25061716 50.82992  86.569    16.6   12.2
## 20  0.7583938 72.10039  0.00428962  0.81396964 48.03618  77.629    16.6    7.4
## 21  4.7222857 64.38262  0.07024216  0.64703537 51.41409  75.008    23.6    2.6
## 22  0.2453268 52.64494  0.19751535  0.72191283 50.09548  29.358    21.7   11.0
## 23  0.2917947 52.25138  0.11175378  4.35178271 50.42199  13.032    22.9   12.1
## 24  0.5703887 64.22991  0.16249798  0.92056413 51.19798  23.388    21.1   10.0
## 25  0.3083562 54.63954  0.25216237  0.53343989 50.00349  56.374    21.6   10.9
## 26  4.3071336 66.89774  0.37058856  0.04075308 50.39153  81.411     9.8    6.1
## 27  0.3528256 52.87991  0.04666377  0.07490412 50.43647  41.364    23.1   10.3
## 28  1.7597254 68.44406  0.49648685  0.44748702 50.92577  80.778    15.8   15.0
## 29  0.3889863 57.45457  0.00832322  4.47244478 49.55870  28.965    22.9   10.9
## 30  0.2877278 55.55478  0.05244363  0.15356846 50.06560  66.916    16.7    9.9
## 31  5.5638202 65.04263  0.04089400  0.73077198 51.85262  56.947    16.7    5.3
## 32  4.0918014 64.99252  0.10625695  1.37602888 50.80859  73.792    15.0    4.7
## 33  4.5174188 63.72878  0.05797446  1.38067302 50.27420  87.874    11.3    3.8
## 34  0.6099089 65.89830  0.00958920  0.41368421 47.36679  77.777    19.6   10.4
## 35  1.5873319 64.94005  0.10627165  2.19978576 50.00780  81.074    19.0   12.0
## 36  1.5376442 64.81412  0.17084357  0.68788682 49.97063  63.821    13.0   12.8
## 37  0.7910340 60.97150  0.98423595  0.98873469 49.46997  42.704    27.7    5.8
## 38  1.8419292 64.58083  0.06420744  3.09881467 53.11400  72.023    14.0   15.4
## 39  0.2886712 60.42552  0.01308974  0.46665740 44.45585  72.143    22.0   10.7
## 40  5.5491132 64.01657  0.01320884  0.30386105 52.85843  68.880    17.0    4.5
## 41  0.5012502 55.71589  1.09224559  1.09224559 49.97889  20.763    18.3   11.7
## 42  0.5795601 65.03770  0.00883483  0.48357033 49.30050  56.248    30.6    5.4
## 43  5.4057724 62.13403  0.05518050  0.18156856 50.72076  85.382    10.2    5.5
## 44  6.1032258 62.00891  0.66987244  1.22338396 51.58424  80.444    10.6    6.4
## 45  0.5335561 59.41022  0.02119275  0.08224764 49.06862  89.370    14.4    9.1
## 46  0.2595557 53.14127  0.02280102  2.25306522 50.40213  61.270    20.4   10.9
## 47  3.6827447 65.33927  0.03731000  0.65275202 52.29124  58.632    24.9    3.6
## 48  6.6270801 64.91701  0.82927922  2.37370970 50.66037  77.312    12.1    4.0
## 49  0.3266130 59.33504  0.29767108  1.30821429 49.32583  56.060    20.8    9.8
## 50  7.2679122 64.27348  0.10727668  0.83224732 50.91620  79.058    12.4    2.9
## 51  1.0548473 60.75253  0.17247807  1.60953779 50.75935  51.054    14.9   15.7
## 52  0.3195615 53.22380  0.12414318  0.50522212 51.82111  36.140    22.4    9.2
## 53  1.2722362 65.33518  0.00779004  0.03957348 49.80802  26.606    30.5   12.4
## 54  0.8276328 61.80835  0.11123176  4.03598549 50.65221  55.278    26.5   12.6
## 55  1.0293956 63.56850  0.09587522  0.85687032 50.04934  57.096    14.0   19.6
## 56  4.2713096 66.43028  0.09768785  1.07906606 52.43243  71.351    23.0    4.4
## 57  0.3130915 50.39318  0.15477751  0.12291734 50.08490  23.059    23.9    9.3
## 58  2.7008155 68.71630  0.18729160  0.25189446 50.72703  87.564    12.4    7.1
## 59  1.7877871 71.20211 13.92730000  1.48348833 48.67937  59.152    17.0    7.0
## 60  0.9448065 66.76674 13.52617328  4.54938073 48.02354  34.030    23.3   11.3
## 61  0.8561815 67.59164  2.67663435  1.47752190 49.64388  55.325    26.4    6.0
## 62  1.1233084 69.33887  0.81800269  0.50222420 49.43913  74.898    14.8   10.1
## 63  0.4831038 58.28983  0.38433600  0.88530570 49.40868  70.473    21.3   28.4
## 64  3.0188525 64.72778  0.04853506  0.70452983 50.42551  63.170    10.3    4.3
## 65  3.0461867 60.09777  0.08883800  4.10526802 50.29813  92.418     9.6    4.1
## 66  7.1734357 63.91920  0.60431283  2.05450748 51.37667  70.438     9.5    3.8
## 67  2.0456797 67.45326  0.02934855  2.70993075 50.33983  55.674    14.7    8.7
## 68  8.3613475 59.72678  1.26529100  3.47073458 51.15926  91.616     8.4    4.8
## 69  0.6023762 61.90802  0.09956011  1.12142498 49.38968  90.979    19.2   10.9
## 70  1.4932044 64.14760  0.18276499  0.06769826 51.51148  57.428    26.8    9.5
## 71  0.2699139 57.87865  0.51393010  0.90299417 50.31602  27.030    13.4    9.6
## 72  0.2327419 75.91064  0.04137309  2.32172222 39.54817 100.000    17.4   12.8
## 73  5.5652960 63.96060  0.01926542  0.30983307 54.01017  68.142    21.9    5.4
## 74  1.4828557 66.90176  0.06848925  6.69494135 49.70581  88.593    17.9    5.8
## 75  0.8209008 62.38256  0.02108132  0.69437813 50.71690  28.153    26.6    8.3
## 76  0.3996440 55.62158  0.04818977  0.50030907 49.76829  51.151    17.6   10.0
## 77  0.7605248 67.28872  0.06678567  0.03795632 49.48379  80.102    20.1   20.1
## 78  5.8370281 65.41254  0.02789533  0.44531351 53.79196  67.679    20.7    6.6
## 79  4.0235270 69.93802  0.00607728  2.50093827 49.53926  90.981    10.0    6.7
## 80  0.4241700 56.34513  0.26262368  0.45139856 50.12397  37.191    22.9   10.6
## 81  0.3087382 53.45228  0.18143315  1.92440762 50.70152  16.937    16.4    8.6
## 82  1.0741699 69.33310  0.31528585  0.95962821 48.57852  76.036    17.2    8.9
## 83  0.2683715 49.94928  0.19077690  0.15635016 49.94100  42.356    24.6    8.9
## 84  0.4204185 56.77571  0.04403319  0.04272164 49.82266  53.672    18.1    9.4
## 85  2.0227961 70.73213  0.01265303  6.23301970 50.57711  40.793    22.6    4.8
## 86  1.5523039 66.21947  1.26190788  0.64914626 51.08928  80.156    15.7   10.3
## 87  2.2672524 72.67035  0.03545883  1.23519804 52.03556  42.629    24.9    5.7
## 88  0.6283914 65.50689  0.03170208  0.02040609 50.66954  68.445    30.2   10.6
## 89  3.4204855 66.81554  0.00622345  0.46271004 50.55901  66.813    20.6    3.5
## 90  1.1687236 65.78072  0.36029138  0.80728519 50.40385  62.453    12.4    6.4
## 91  0.3481273 52.43844  0.29495962  0.37508535 51.47519  35.988    18.4    7.8
## 92  0.7791075 67.84431  0.53708395  0.82238615 51.80790  30.579    24.2    8.6
## 93  0.6151794 59.45372  0.02448255  0.02973746 51.55369  50.032    21.3    9.8
## 94  0.7516195 63.85806  0.28087871  1.95939107 54.53534  19.740    21.8    8.8
## 95  4.6976392 64.69565  0.17231017  5.11457910 50.22094  91.490    11.2    5.2
## 96  3.7311137 64.69414  0.04885500  0.18554176 50.83777  86.538    10.1    6.0
## 97  1.1025688 64.55082  0.06465513  0.53727048 50.71271  58.522    14.2   12.7
## 98  0.2340882 47.42067  0.22442948  0.17717651 49.77100  16.425    20.0   10.4
## 99  4.2231531 65.40174  0.05314336  0.14554920 49.52463  82.248     9.2    5.6
## 100 0.4596337 75.36071  0.04829483  0.15604145 34.01408  84.539    17.8   17.7
## 101 0.6545620 60.41741  2.12215030  2.75289319 48.53807  36.666    24.7    7.3
## 102 1.9205337 64.83296  0.04176873  0.56186077 49.90538  67.709    13.0    9.7
## 103 1.2135437 64.12928  0.06956071  0.17508359 49.15161  61.585    17.5   12.0
## 104 1.6809015 66.12100  0.31989256  0.24991606 50.33776  77.907    12.6   10.5
## 105 0.7893793 63.91439  1.06651922  3.57688305 49.74166  46.907    26.8    7.5
## 106 4.4631164 67.42991  0.37978548  1.24035886 51.53071  60.058    18.7    4.7
## 107 6.3408699 64.58823  0.10281762  1.12239454 52.71196  65.211    11.1    4.2
## 108 0.1847213 85.08917  0.02781677  2.39593196 24.49529  99.135    15.3   25.9
## 109 4.7084438 66.12674  0.19473936  0.84639847 51.34374  53.998    21.4    3.6
## 110 0.3624706 57.08623  0.12301939  4.98659870 50.86106  17.211    18.2   13.5
## 111 2.4482249 67.86891  0.00110210  2.82589744 49.20878  52.198    23.2    5.6
## 112 0.5038453 71.64306  0.33699947  0.15676654 42.44585  83.844    16.4   16.3
## 113 0.3825330 53.85775  0.15854360  0.82347478 51.27733  47.192    18.1   12.2
## 114 4.0191136 65.96453  0.06982084  0.79831740 51.00252  56.092    19.1    3.0
## 115 0.3522140 55.97380  0.07650154  1.05987171 50.12127  42.055    30.5    8.9
## 116 2.1766595 76.25834  0.05638676 79.52998418 47.65813 100.000     9.3    3.7
## 117 3.2223629 68.92462  0.05447011  1.13290578 51.33833  53.726    17.2    6.0
## 118 5.3152643 65.37135  0.02067372  1.02639860 50.24521  54.541    12.7    6.6
## 119 0.7184723 65.60251  0.57779622  0.47630120 50.69415  66.355    26.2    9.1
## 120 3.2120425 72.60812  0.51635256  5.29652104 49.91688  81.459     7.8   10.0
## 121 6.1672057 65.95449  0.46723749  0.93529058 50.89664  80.321     9.9    3.5
## 122 1.6065781 65.32978  0.21670000  3.45558922 51.96682  18.476    17.4    9.7
## 123 5.1726032 62.32269  0.10183175  0.25001043 49.94578  87.431     9.1    4.9
## 124 5.1493834 66.46583  0.08516543  2.15521378 50.42712  73.797     8.6    6.1
## 125 0.5234433 60.19383  0.09100837  0.65572714 49.58384  27.134    25.3    7.6
## 126 2.5315654 71.01212  0.69428524  1.35897207 51.26870  49.949    14.5   10.2
## 127 0.2700138 55.79601  0.07889094  1.45046773 50.26805  41.702    23.6   10.7
## 128 1.7197442 67.51420  0.11565204  0.74441323 50.43715  68.945    16.1    6.4
## 129 1.6774797 66.86738  0.82319724  1.06960129 50.67781  75.143    16.1    6.2
## 130 0.2057545 51.12849  0.42723139  2.13061734 50.77608  23.774    21.9   12.7
## 131 3.9876216 67.75290  0.44622516  0.77029667 53.68775  69.352    24.7    5.0
## 132 0.1307769 84.31149  0.09630959  1.35609110 30.63669  86.522    16.8   16.8
## 133 5.0282090 63.92605  0.66488991  2.74827392 50.63527  83.398    10.9    3.5
## 134 3.8751036 65.48331  3.27167434  0.35766089 50.52001  82.256    14.6    6.6
## 135 4.3947641 64.57750  0.03449299  0.19708028 51.72154  95.334    16.7    7.5
## 136 0.8369038 66.89480  0.32955400  0.77469205 50.13736  50.478    24.5    6.0
## 137 0.3498816 57.50884  0.28498687  0.53977853 49.61166  36.642    30.6   14.7
## 138 0.2529708 52.96418  0.17351822  0.23341479 50.49321  43.521    17.9   10.2
## 139 0.4222739 54.65941  0.14439018  0.37324591 52.35675  32.209    19.3   12.3
##     Tuberculosis Diabetes ImmunSaramp HipTen.M BCG    Medicos      Camas
## 1          189.0      9.2          64     19.8   1 0.24009091  0.4363636
## 2           18.0      9.0          94     39.4   1 1.21237143  2.9375000
## 3           69.0      6.7          80     23.0   1 1.31202500  1.9000000
## 4          355.0      4.5          50     25.2   1 0.17300000  0.8000000
## 5           27.0      5.9          94     32.9   1 3.57165000  4.6000000
## 6           31.0      6.1          95     43.4   1 3.06122500  4.0200000
## 7            6.6      5.6          95     32.8   1 3.27402222  3.8783333
## 8            7.1      6.6          94     38.8   1 4.66801111  7.7000000
## 9           63.0      6.1          96     31.4   1 3.58736250  6.4666667
## 10         221.0      9.2          97     17.4   1 0.39047692  0.5750000
## 11          31.0      5.0          97     45.5   1 4.22154000 11.2000000
## 12           9.0      4.6          96     35.0   0 2.79814444  6.5500000
## 13          30.0     17.1          97     25.1   1 0.96785000  1.1600000
## 14          56.0      1.0          71     40.3   1 0.11874000  0.5000000
## 15         149.0     10.3          97     20.4   1 0.26431111  1.7333333
## 16         108.0      6.8          89     27.2   1 0.71612500  1.1000000
## 17          25.0      9.0          68     43.2   1 1.77503333  3.3666667
## 18         275.0      5.8          97     41.0   1 0.35774444  2.0000000
## 19          45.0     10.4          84     38.5   1 1.84654000  2.3285714
## 20          68.0     13.3          99     32.0   1 1.38302727  2.7542857
## 21          22.0      6.0          93     46.5   1 3.80887500  6.4888889
## 22          48.0      7.3          88     12.4   1 0.04237143  0.6500000
## 23         111.0      5.1          88     30.8   1 0.04885000  1.1333333
## 24         302.0      6.4          84     24.1   1 0.24971429  0.7600000
## 25         186.0      6.0          71     17.9   1 0.07534000  1.4000000
## 26           5.6      7.6          90     23.9   0 2.32944444  3.0714286
## 27         540.0      6.0          49     33.2   1 0.04973333  1.1000000
## 28          33.0      7.4          95     28.7   1 1.70129231  1.3166667
## 29          35.0     12.3          90     41.5   1 0.18323333  2.2000000
## 30         375.0      6.0          75     19.3   1 0.12880000  1.6000000
## 31           8.4      5.4          93     38.5   1 2.80697000  5.6354545
## 32           5.4      7.0          96     42.6   1 3.70292500  7.0100000
## 33           5.4      8.3          95     34.6   1 3.56958333  3.4111111
## 34         260.0      5.1          86     22.3   1 0.21512500  1.4571429
## 35          45.0      8.6          95     29.3   1 1.37733333  1.4375000
## 36          44.0      5.5          83     28.6   1 1.90360000  1.5285714
## 37          12.0     17.2          94     23.9   1 1.73048750  1.5100000
## 38          70.0      8.8          81     30.0   1 1.65428000  0.9900000
## 39         201.0      6.0          30     25.2   1 0.40000000  2.0666667
## 40          13.0      4.2          87     42.9   1 3.34070000  5.3600000
## 41         151.0      4.3          61     22.2   1 0.03620000  1.7500000
## 42          54.0     14.7          94     25.0   1 0.58042000  2.1475000
## 43           4.7      5.6          96     46.6   1 3.14394000  5.9600000
## 44           8.9      4.8          90     38.5   1 3.26508571  6.9222222
## 45         525.0      6.0          59     43.6   1 0.36110000  3.2000000
## 46         174.0      1.9          91     30.4   1 0.09858333  1.0000000
## 47          80.0      5.8          98     41.0   1 4.62520000  3.1222222
## 48           7.3     10.4          97     34.2   1 3.84022000  8.2555556
## 49         148.0      2.5          92     41.4   1 0.12015714  0.9000000
## 50           4.5      4.7          97     36.4   1 5.71011111  4.6555556
## 51          26.0     10.0          87     26.5   1 0.62945000  0.6250000
## 52         176.0      2.4          48     41.0   1 0.09196667  0.3000000
## 53          83.0     11.6          98     26.3   1 0.56823333  2.1600000
## 54         176.0      6.7          69     26.7   1 0.18585000  1.0000000
## 55          37.0      7.3          89     25.7   1 0.57160000  0.7285714
## 56           6.4      6.9          99     47.4   1 3.24861667  7.3000000
## 57         142.0      6.0          37     31.3   1 0.04200000  0.4000000
## 58          18.0      8.6          93     28.2   1 1.03645000  2.1714286
## 59          61.0      9.2          99     32.3   1 1.57320000  3.4066667
## 60         199.0     10.4          90     29.2   1 0.67390833  0.8000000
## 61         316.0      6.3          75     24.7   1 0.24655000  0.9000000
## 62          14.0      9.6          99     25.9   1 0.98516000  1.4400000
## 63          42.0      8.8          83     23.9   1 0.72533333  1.3100000
## 64           7.0      3.2          92     32.2   1 2.81283000  3.9555556
## 65           4.0      9.7          98     40.1   1 3.38105556  3.5333333
## 66           7.0      5.0          93     36.5   0 3.94927778  3.6875000
## 67           2.9     11.3          89     33.6   1 0.54227143  1.7571429
## 68          14.0      5.6          97     37.5   1 2.27806667 13.7960000
## 69           5.0     12.7          92     26.3   1 2.24923636  1.7818182
## 70          68.0      6.1          99     33.1   1 3.60587000  7.4111111
## 71         292.0      3.1          89     36.8   1 0.18122857  1.4000000
## 72          23.0     12.2          99     26.5   1 2.16873636  1.9800000
## 73          29.0      5.0          98     36.7   1 3.38225000  6.8111111
## 74          11.0     11.2          82     29.6   0 2.55344444  3.3800000
## 75         611.0      4.5          90     35.0   1 0.06760000  1.3000000
## 76         308.0      2.4          91     40.3   1 0.02466667  0.7500000
## 77          40.0     10.2          97     23.6   1 1.98748333  3.6700000
## 78          44.0      3.8          92     40.5   1 4.08613000  7.1666667
## 79           8.0      5.0          99     37.1   1 2.85313000  5.3727273
## 80         233.0      4.5          62     32.2   1 0.17360000  0.2500000
## 81         181.0      4.5          87     32.6   1 0.01767500  1.2000000
## 82          92.0     16.7          96     25.2   1 1.24358000  1.8312500
## 83          53.0      2.4          70     32.1   1 0.09575714  0.3333333
## 84          93.0      7.1          78     29.5   1 0.14066667  0.4000000
## 85          13.0     22.0          99     48.4   1 1.45775556  3.2333333
## 86          23.0     13.5          97     28.5   1 2.03156667  1.5888889
## 87          86.0      5.7          93     42.2   1 2.62560000  6.1666667
## 88         428.0      4.7          99     30.6   1 2.96358889  6.3266667
## 89          15.0      9.0          58     41.9   1 2.08927778  4.0125000
## 90          99.0      7.0          99     33.0   1 0.63655000  0.9800000
## 91         551.0      3.3          85     31.5   1 0.04688750  0.7666667
## 92         338.0      3.9          93     25.5   1 0.53712000  0.7500000
## 93         524.0      4.5          82     41.9   1 0.37305000  3.0000000
## 94         151.0      7.2          91     22.1   1 0.57297500  2.6500000
## 95           5.3      5.4          93     33.8   0 3.40828889  4.6000000
## 96           7.3      6.2          92     30.9   1 2.70726000  2.5500000
## 97          41.0     11.4          99     26.2   1 0.74390000  0.9222222
## 98          87.0      2.4          77     32.4   1 0.03340000  0.3000000
## 99           4.1      5.3          96     33.0   1 4.21626667  4.2555556
## 100          5.9     10.1          99     26.2   1 1.95183077  1.8600000
## 101        265.0     19.9          76     21.1   1 0.84727500  0.7200000
## 102         52.0      7.7          98     24.6   1 1.43397000  2.2888889
## 103         43.0      9.6          93     27.2   1 0.96393333  1.2857143
## 104        123.0      6.6          85     17.8   1 1.18280000  1.4875000
## 105        554.0      7.1          67     23.5   1 1.25186667  0.5833333
## 106         16.0      6.1          93     35.0   1 2.19399000  6.5222222
## 107         24.0      9.8          99     35.7   1 3.79097273  3.4222222
## 108         31.0     15.6          99     36.2   1 2.47302500  1.6400000
## 109         68.0      6.9          90     46.7   1 2.40146667  6.4555556
## 110         59.0      5.1          99     30.4   1 0.09168889  1.6000000
## 111          6.3     11.6          99     30.7   1 0.65870000  3.3000000
## 112         10.0     15.8          98     27.0   1 2.25626250  2.2500000
## 113        118.0      2.4          82     40.4   1 0.14020000  0.2000000
## 114         17.0      9.0          92     41.6   1 2.42901111  5.6125000
## 115        298.0      2.4          80     39.7   1 0.02050000  0.4000000
## 116         47.0      5.5          95     22.1   1 1.76859091  2.7457143
## 117          5.8      6.5          96     33.7   1 3.11790000  6.3400000
## 118          5.3      5.9          93     37.2   1 2.59476000  4.6555556
## 119        520.0     12.7          70     45.9   1 0.76988750  2.8000000
## 120         66.0      6.9          98     23.5   1 2.05984615 10.6080000
## 121          9.4      6.9          97     37.1   1 4.15920000  3.1888889
## 122         64.0     10.7          99     25.8   1 0.77147000  3.5500000
## 123          5.5      4.8          97     37.0   0 4.03310000  2.7666667
## 124          6.4      5.7          96     31.7   1 4.00455000  5.1222222
## 125         84.0      6.1          98     25.4   1 1.78443333  5.2666667
## 126        153.0      7.0          96     23.8   1 0.40473333  2.1000000
## 127         36.0      2.4          85     40.1   1 0.08897500  0.8000000
## 128         35.0      8.5          96     25.2   1 1.16097143  2.0545455
## 129         16.0     11.1          96     35.1   1 1.65210000  2.4555556
## 130        200.0      2.5          86     32.7   1 0.10457500  0.7500000
## 131         80.0      6.1          91     50.6   1 3.35196667  8.9222222
## 132          1.0     16.3          99     19.2   1 1.70489167  1.6777778
## 133          8.0      3.9          92     29.9   1 2.75287500  3.2111111
## 134          3.0     10.8          92     31.8   0 2.52747500  3.0333333
## 135         33.0      7.3          97     38.8   1 4.16766000  2.5250000
## 136         70.0      6.5          96     27.7   1 2.50781250  4.5888889
## 137         48.0      5.4          64     12.4   1 0.30986667  0.6900000
## 138        346.0      4.5          94     27.4   1 0.08791250  1.9500000
## 139        210.0      1.8          88     32.0   1 0.06561250  2.3500000
##             PBI TempMarzo l10muertes.permil
## 1     1.8351696      7.60        0.85166698
## 2    11.3351950      6.04        1.09735434
## 3    14.1967389     17.91        1.19736588
## 4     6.7205961     22.78        0.05301271
## 5    20.0684923     17.51        1.08768715
## 6     8.3491802     -0.57        1.53403057
## 7    45.7525548     25.37        0.70937665
## 8    48.9687140      1.42        1.86867832
## 9    17.0906963      4.97        0.80830014
## 10    3.3061083     25.42        0.64062316
## 11   18.1721809     -0.69        1.37219412
## 12   45.2631622      5.23        2.91424609
## 13    8.0937796     24.45        0.79385771
## 14    2.0675705     30.14        0.10078712
## 15    8.3417246      5.68        0.00000000
## 16    6.5317860     22.07        1.40930364
## 17   11.6971771      4.01        1.66678153
## 18   16.1336867     24.30        0.15945618
## 19   15.5847506     25.49        2.09062497
## 20   80.8004129     25.92        0.75300190
## 21   17.9465777      4.70        1.29960529
## 22    1.6709928     30.63        0.56624131
## 23    0.7568378     20.43        0.03722023
## 24    3.3333517     27.93        0.00000000
## 25    3.2930885     26.27        0.90413575
## 26   44.2264907    -18.72        2.26374838
## 27    0.8532664     26.96        0.08432564
## 28   13.2116325     25.14        1.23486238
## 29    2.6420144     25.20        0.53185125
## 30    5.6636489     25.60        0.66491803
## 31   22.9922117      5.83        1.40989932
## 32   32.7718057      2.80        1.48902061
## 33   48.5249925      2.24        1.99324531
## 34    2.7442687     25.75        1.29603122
## 35   13.9050879     22.88        1.65899033
## 36   10.8759023     21.93        2.28487232
## 37   10.8110341     17.83        0.96804820
## 38    7.2348467     25.70        0.84966889
## 39   30.5908474     25.04        1.00721356
## 40   28.8340001     -2.35        1.70728513
## 41    1.5126247     23.48        0.02322475
## 42    8.8754441     25.00        0.00000000
## 43   42.8552430     -6.09        1.76134842
## 44   40.3515680      6.37        2.63132934
## 45   17.0778163     26.14        0.88115819
## 46    2.4051919     27.32        0.15793307
## 47    9.5834608      0.72        0.62493108
## 48   46.5762068      3.87        2.01040779
## 49    3.9219764     29.52        0.33086008
## 50   27.2065489      8.19        1.23366924
## 51    7.5159291     22.94        0.69394933
## 52    2.0022876     27.63        0.43000974
## 53    7.1987245     25.65        1.17956888
## 54    1.7058976     23.47        0.60817085
## 55    4.4336960     23.38        1.32704478
## 56   25.7573736      5.44        1.72513328
## 57    2.0073216     26.55        0.71053771
## 58   22.2523960     10.87        1.66184336
## 59   13.5313781      0.49        0.63650295
## 60    5.8366566     23.45        0.63846947
## 61   10.5772045     25.79        0.81312576
## 62   18.4501839     11.33        1.97066822
## 63   15.8895142     15.12        0.74455172
## 64   59.3055780      6.00        2.52768884
## 65   34.5710854     14.96        1.51362517
## 66   37.7630816      6.52        2.73899149
## 67    8.5085469     23.44        0.60923050
## 68   39.1530060      2.52        0.89103814
## 69    9.2014965     13.08        0.27966158
## 70   24.1030338     -3.96        0.48064752
## 71    2.8839337     26.10        0.31600904
## 72   75.8307296     19.23        1.63646809
## 73   23.8146826     -1.37        1.11188355
## 74   13.2450688     10.39        0.68089875
## 75    2.9043623     15.13        0.00000000
## 76    1.2563111     26.44        0.81973136
## 77   19.3396159     17.76        0.20382969
## 78   27.8087523     -0.47        1.39199086
## 79  100.2191161      4.70        2.26007623
## 80    1.7044431     23.99        0.03187466
## 81    1.1836484     23.25        0.08652600
## 82   25.8714034     25.19        0.66721788
## 83    2.0131236     27.34        0.66924316
## 84    3.8172107     25.29        0.66592067
## 85   19.3987280     25.48        0.94954834
## 86   17.8555895     18.44        1.83964772
## 87    5.8933274      2.93        1.89361039
## 88   11.1277975     -8.80        0.00000000
## 89   16.2734892      2.53        1.18924972
## 90    7.4795258     13.09        0.81997625
## 91    1.2616857     25.48        0.01447977
## 92    5.0214146     22.74        0.04599322
## 93   10.2230967     22.67        0.00000000
## 94    2.4752041     10.02        0.05782208
## 95   49.9843155      4.95        2.53367393
## 96   36.4995043     13.60        0.74060910
## 97    4.8837325     24.95        0.80708405
## 98    0.9278714     26.01        0.58564967
## 99   62.6503184     -5.49        1.65533067
## 100  42.4790524     23.40        0.94784219
## 101   4.6501098     16.01        0.84119511
## 102  20.8839055     25.44        1.88318015
## 103  11.4029323     25.84        0.41184731
## 104  12.3515166     19.99        2.09868025
## 105   7.0082665     25.13        0.97663292
## 106  25.9904310      2.65        1.44821011
## 107  29.3390041     11.33        2.12347272
## 108 123.2139364     21.78        1.07132447
## 109  21.6182719      3.40        1.80343313
## 110   1.7889788     19.25        0.00000000
## 111  10.9317631     25.90        0.00000000
## 112  51.5878305     20.60        1.13389947
## 113   3.1310271     28.47        0.53906244
## 114  14.9080484      4.91        1.54868038
## 115   1.4959394     27.65        0.83772947
## 116  86.0684237     28.62        0.70577580
## 117  29.0915206      2.40        0.78819906
## 118  31.7402860      3.38        1.72227622
## 119  12.8666891     21.10        1.02339821
## 120  34.6370853      3.66        0.79306494
## 121  34.5888453      8.60        2.76446761
## 122  11.1185327     27.03        0.16478915
## 123  47.6285919     -4.98        2.61847597
## 124  61.3146089      0.09        2.28867018
## 125   2.7293337     -3.08        0.78207686
## 126  15.8571484     27.38        0.26030714
## 127   1.4956789     29.31        0.42289247
## 128  11.2660561     14.21        0.71183934
## 129  23.5212137      4.77        1.73570816
## 130   1.8103287     23.68        0.00000000
## 131   8.4417522      1.18        1.18842716
## 132  65.5180899     22.61        1.43897112
## 133  41.1611271      4.66        2.75158819
## 134  55.0581658      0.06        2.48855760
## 135  20.4797528     21.26        0.86794498
## 136   6.8361061      5.38        0.15375896
## 137   3.5314239     20.82        0.45632576
## 138   3.8025013     22.90        0.14718638
## 139   2.5606953     22.92        0.10620013
modelo_l10muertes<-lm(l10muertes.permil~.,data = df_covid_l10muertes)
summary(modelo_l10muertes)
## 
## Call:
## lm(formula = l10muertes.permil ~ ., data = df_covid_l10muertes)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.03352 -0.32453  0.00078  0.28469  1.18822 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2074581  1.4481831  -0.143 0.886328    
## Pobla80       0.1678347  0.0491913   3.412 0.000878 ***
## PoblaMid      0.0179866  0.0113805   1.580 0.116610    
## PoblaData     0.0015105  0.0252924   0.060 0.952477    
## PoblaDens    -0.0135871  0.0069675  -1.950 0.053481 .  
## Mujeres       0.0189388  0.0206484   0.917 0.360862    
## Urbano        0.0059219  0.0030802   1.923 0.056884 .  
## DisMort      -0.0104358  0.0117804  -0.886 0.377449    
## Lesion        0.0007857  0.0140986   0.056 0.955648    
## Tuberculosis -0.0007458  0.0003891  -1.917 0.057647 .  
## Diabetes      0.0012350  0.0142983   0.086 0.931310    
## ImmunSaramp  -0.0080585  0.0040730  -1.979 0.050142 .  
## HipTen.M      0.0035230  0.0064671   0.545 0.586924    
## BCG          -0.3865863  0.2136311  -1.810 0.072840 .  
## Medicos      -0.0064583  0.0677158  -0.095 0.924176    
## Camas        -0.0913157  0.0294257  -3.103 0.002383 ** 
## PBI           0.0066034  0.0038957   1.695 0.092639 .  
## TempMarzo    -0.0113363  0.0066844  -1.696 0.092470 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4786 on 121 degrees of freedom
## Multiple R-squared:  0.6495, Adjusted R-squared:  0.6002 
## F-statistic: 13.19 on 17 and 121 DF,  p-value: < 2.2e-16

El modelo con todas las variables introducidas como predictores tiene un \(R^2\) alta (0.6495), es capaz de explicar el 65% de la variabilidad observada en la muertes logarimicas por mil de covid. El p-value del modelo es significativo (2.2e-16) por lo que se puede aceptar que el modelo no es por azar, al menos uno de los coeficientes parciales de regresión es distinto de 0. Muchos de ellos no son significativos, lo que es un indicativo de que podrían no contribuir al modelo.

step(object = modelo_l10muertes, direction = "both", trace = 1)
## Start:  AIC=-188.14
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaData + PoblaDens + 
##     Mujeres + Urbano + DisMort + Lesion + Tuberculosis + Diabetes + 
##     ImmunSaramp + HipTen.M + BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - Lesion        1   0.00071 27.714 -190.14
## - PoblaData     1   0.00082 27.715 -190.14
## - Diabetes      1   0.00171 27.715 -190.13
## - Medicos       1   0.00208 27.716 -190.13
## - HipTen.M      1   0.06797 27.782 -189.80
## - DisMort       1   0.17974 27.893 -189.25
## - Mujeres       1   0.19268 27.906 -189.18
## <none>                      27.714 -188.14
## - PoblaMid      1   0.57211 28.286 -187.30
## - PBI           1   0.65807 28.372 -186.88
## - TempMarzo     1   0.65876 28.372 -186.88
## - BCG           1   0.75002 28.464 -186.43
## - Tuberculosis  1   0.84134 28.555 -185.99
## - Urbano        1   0.84659 28.560 -185.96
## - PoblaDens     1   0.87098 28.585 -185.84
## - ImmunSaramp   1   0.89658 28.610 -185.72
## - Camas         1   2.20570 29.919 -179.50
## - Pobla80       1   2.66622 30.380 -177.38
## 
## Step:  AIC=-190.14
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaData + PoblaDens + 
##     Mujeres + Urbano + DisMort + Tuberculosis + Diabetes + ImmunSaramp + 
##     HipTen.M + BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - PoblaData     1   0.00079 27.715 -192.14
## - Diabetes      1   0.00157 27.716 -192.13
## - Medicos       1   0.00230 27.717 -192.13
## - HipTen.M      1   0.06828 27.783 -191.80
## - DisMort       1   0.17952 27.894 -191.24
## - Mujeres       1   0.20304 27.918 -191.13
## <none>                      27.714 -190.14
## - PoblaMid      1   0.57359 28.288 -189.29
## - PBI           1   0.65765 28.372 -188.88
## - TempMarzo     1   0.66083 28.375 -188.87
## - BCG           1   0.74942 28.464 -188.43
## + Lesion        1   0.00071 27.714 -188.14
## - Urbano        1   0.86298 28.577 -187.88
## - Tuberculosis  1   0.87196 28.586 -187.84
## - ImmunSaramp   1   0.89703 28.611 -187.71
## - PoblaDens     1   0.92225 28.637 -187.59
## - Camas         1   2.20737 29.922 -181.49
## - Pobla80       1   2.80563 30.520 -178.74
## 
## Step:  AIC=-192.14
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaDens + Mujeres + 
##     Urbano + DisMort + Tuberculosis + Diabetes + ImmunSaramp + 
##     HipTen.M + BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - Diabetes      1   0.00176 27.717 -194.13
## - Medicos       1   0.00247 27.718 -194.12
## - HipTen.M      1   0.06820 27.783 -193.79
## - DisMort       1   0.18151 27.897 -193.23
## - Mujeres       1   0.20228 27.918 -193.13
## <none>                      27.715 -192.14
## - PoblaMid      1   0.58776 28.303 -191.22
## - PBI           1   0.66055 28.376 -190.86
## - TempMarzo     1   0.67640 28.392 -190.78
## - BCG           1   0.75117 28.466 -190.42
## + PoblaData     1   0.00079 27.714 -190.14
## + Lesion        1   0.00069 27.715 -190.14
## - Urbano        1   0.86356 28.579 -189.87
## - Tuberculosis  1   0.87334 28.589 -189.82
## - ImmunSaramp   1   0.89671 28.612 -189.71
## - PoblaDens     1   0.92193 28.637 -189.59
## - Camas         1   2.22252 29.938 -183.41
## - Pobla80       1   2.82059 30.536 -180.66
## 
## Step:  AIC=-194.13
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaDens + Mujeres + 
##     Urbano + DisMort + Tuberculosis + ImmunSaramp + HipTen.M + 
##     BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - Medicos       1   0.00266 27.720 -196.11
## - HipTen.M      1   0.06659 27.784 -195.79
## - DisMort       1   0.18170 27.899 -195.22
## - Mujeres       1   0.20142 27.918 -195.12
## <none>                      27.717 -194.13
## - PBI           1   0.66269 28.380 -192.84
## - TempMarzo     1   0.67814 28.395 -192.77
## - PoblaMid      1   0.73463 28.452 -192.49
## - BCG           1   0.75604 28.473 -192.39
## + Diabetes      1   0.00176 27.715 -192.14
## + PoblaData     1   0.00099 27.716 -192.13
## + Lesion        1   0.00054 27.716 -192.13
## - Urbano        1   0.86683 28.584 -191.85
## - ImmunSaramp   1   0.89496 28.612 -191.71
## - Tuberculosis  1   0.94198 28.659 -191.48
## - PoblaDens     1   0.95492 28.672 -191.42
## - Camas         1   2.24964 29.967 -185.28
## - Pobla80       1   2.82104 30.538 -182.66
## 
## Step:  AIC=-196.11
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaDens + Mujeres + 
##     Urbano + DisMort + Tuberculosis + ImmunSaramp + HipTen.M + 
##     BCG + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - HipTen.M      1    0.0645 27.784 -197.79
## - Mujeres       1    0.1995 27.919 -197.12
## - DisMort       1    0.2058 27.925 -197.09
## <none>                      27.720 -196.11
## - PBI           1    0.6706 28.390 -194.79
## - TempMarzo     1    0.7302 28.450 -194.50
## - PoblaMid      1    0.7324 28.452 -194.49
## - BCG           1    0.7734 28.493 -194.29
## + Medicos       1    0.0027 27.717 -194.13
## + Diabetes      1    0.0019 27.718 -194.12
## + PoblaData     1    0.0012 27.718 -194.12
## + Lesion        1    0.0007 27.719 -194.12
## - ImmunSaramp   1    0.9355 28.655 -193.50
## - Tuberculosis  1    0.9509 28.671 -193.43
## - Urbano        1    0.9517 28.671 -193.42
## - PoblaDens     1    0.9526 28.672 -193.42
## - Camas         1    2.2508 29.971 -187.26
## - Pobla80       1    3.3617 31.081 -182.20
## 
## Step:  AIC=-197.79
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaDens + Mujeres + 
##     Urbano + DisMort + Tuberculosis + ImmunSaramp + BCG + Camas + 
##     PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - DisMort       1    0.1754 27.960 -198.92
## - Mujeres       1    0.2199 28.004 -198.69
## <none>                      27.784 -197.79
## - PoblaMid      1    0.7011 28.485 -196.33
## - TempMarzo     1    0.7012 28.485 -196.33
## - PBI           1    0.7135 28.498 -196.27
## - BCG           1    0.7441 28.528 -196.12
## + HipTen.M      1    0.0645 27.720 -196.11
## + Lesion        1    0.0009 27.783 -195.80
## + PoblaData     1    0.0008 27.783 -195.79
## + Medicos       1    0.0006 27.784 -195.79
## + Diabetes      1    0.0002 27.784 -195.79
## - Tuberculosis  1    0.9140 28.698 -195.29
## - ImmunSaramp   1    0.9356 28.720 -195.19
## - Urbano        1    0.9817 28.766 -194.96
## - PoblaDens     1    1.0339 28.818 -194.71
## - Camas         1    2.1864 29.971 -189.26
## - Pobla80       1    3.8037 31.588 -181.96
## 
## Step:  AIC=-198.92
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaDens + Mujeres + 
##     Urbano + Tuberculosis + ImmunSaramp + BCG + Camas + PBI + 
##     TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - Mujeres       1    0.2660 28.226 -199.60
## <none>                      27.960 -198.92
## - PoblaMid      1    0.5907 28.550 -198.01
## - TempMarzo     1    0.6123 28.572 -197.91
## + DisMort       1    0.1754 27.784 -197.79
## - BCG           1    0.7648 28.724 -197.16
## + HipTen.M      1    0.0342 27.925 -197.09
## + Medicos       1    0.0183 27.941 -197.01
## + Diabetes      1    0.0037 27.956 -196.94
## + PoblaData     1    0.0031 27.956 -196.93
## + Lesion        1    0.0000 27.960 -196.92
## - ImmunSaramp   1    0.8268 28.786 -196.87
## - Tuberculosis  1    1.0010 28.961 -196.03
## - PoblaDens     1    1.0258 28.985 -195.91
## - PBI           1    1.0501 29.010 -195.79
## - Urbano        1    1.3864 29.346 -194.19
## - Camas         1    2.6052 30.565 -188.53
## - Pobla80       1    4.7997 32.759 -178.90
## 
## Step:  AIC=-199.6
## l10muertes.permil ~ Pobla80 + PoblaMid + PoblaDens + Urbano + 
##     Tuberculosis + ImmunSaramp + BCG + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## - PoblaMid      1    0.3900 28.616 -199.69
## <none>                      28.226 -199.60
## + Mujeres       1    0.2660 27.960 -198.92
## + DisMort       1    0.2216 28.004 -198.69
## - ImmunSaramp   1    0.7046 28.930 -198.17
## - TempMarzo     1    0.7356 28.961 -198.02
## - BCG           1    0.7631 28.989 -197.89
## + HipTen.M      1    0.0474 28.178 -197.83
## + Lesion        1    0.0253 28.200 -197.72
## - PBI           1    0.7993 29.025 -197.72
## + Medicos       1    0.0141 28.212 -197.67
## + Diabetes      1    0.0082 28.217 -197.64
## - PoblaDens     1    0.8225 29.048 -197.61
## + PoblaData     1    0.0009 28.225 -197.60
## - Tuberculosis  1    0.9039 29.129 -197.22
## - Urbano        1    1.4378 29.663 -194.69
## - Camas         1    2.4931 30.719 -189.84
## - Pobla80       1    7.4770 35.703 -168.94
## 
## Step:  AIC=-199.69
## l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + Tuberculosis + 
##     ImmunSaramp + BCG + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq    RSS     AIC
## <none>                      28.616 -199.69
## - ImmunSaramp   1    0.4246 29.040 -199.65
## + PoblaMid      1    0.3900 28.226 -199.60
## - BCG           1    0.6338 29.249 -198.65
## - PoblaDens     1    0.7271 29.343 -198.21
## + DisMort       1    0.0889 28.527 -198.12
## + Mujeres       1    0.0653 28.550 -198.01
## + Diabetes      1    0.0482 28.567 -197.93
## + Lesion        1    0.0301 28.585 -197.84
## + HipTen.M      1    0.0275 28.588 -197.83
## + PoblaData     1    0.0214 28.594 -197.80
## + Medicos       1    0.0052 28.610 -197.72
## - TempMarzo     1    0.8959 29.512 -197.41
## - Tuberculosis  1    0.9887 29.604 -196.97
## - PBI           1    1.2892 29.905 -195.57
## - Urbano        1    2.0146 30.630 -192.24
## - Camas         1    2.4039 31.019 -190.48
## - Pobla80       1    7.0903 35.706 -170.92
## 
## Call:
## lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + 
##     Tuberculosis + ImmunSaramp + BCG + Camas + PBI + TempMarzo, 
##     data = df_covid_l10muertes)
## 
## Coefficients:
##  (Intercept)       Pobla80     PoblaDens        Urbano  Tuberculosis  
##    1.3368035     0.1921847    -0.0113645     0.0077415    -0.0007509  
##  ImmunSaramp           BCG         Camas           PBI     TempMarzo  
##   -0.0049163    -0.3482976    -0.0906251     0.0065191    -0.0118475


El mejor modelo resultante del proceso de selección ha sido:

l10muertes_modelo <- (lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + Tuberculosis + ImmunSaramp + BCG + Camas + PBI +TempMarzo, 
    data = df_covid_l10muertes))
summary(l10muertes_modelo)
## 
## Call:
## lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + 
##     Tuberculosis + ImmunSaramp + BCG + Camas + PBI + TempMarzo, 
##     data = df_covid_l10muertes)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.02072 -0.28370 -0.03759  0.27169  1.28314 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.3368035  0.4044392   3.305  0.00123 ** 
## Pobla80       0.1921847  0.0339934   5.654 9.63e-08 ***
## PoblaDens    -0.0113645  0.0062769  -1.811  0.07254 .  
## Urbano        0.0077415  0.0025688   3.014  0.00311 ** 
## Tuberculosis -0.0007509  0.0003557  -2.111  0.03669 *  
## ImmunSaramp  -0.0049163  0.0035536  -1.383  0.16891    
## BCG          -0.3482976  0.2060498  -1.690  0.09337 .  
## Camas        -0.0906251  0.0275296  -3.292  0.00128 ** 
## PBI           0.0065191  0.0027041   2.411  0.01733 *  
## TempMarzo    -0.0118475  0.0058951  -2.010  0.04655 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.471 on 129 degrees of freedom
## Multiple R-squared:  0.6381, Adjusted R-squared:  0.6128 
## F-statistic: 25.27 on 9 and 129 DF,  p-value: < 2.2e-16

Es recomendable mostrar el intervalo de confianza para cada uno de los coeficientes parciales de regresión:

confint(lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + Tuberculosis + ImmunSaramp + BCG + Camas + PBI +TempMarzo, 
    data = df_covid_l10muertes))
##                     2.5 %        97.5 %
## (Intercept)   0.536610663  2.136996e+00
## Pobla80       0.124927937  2.594414e-01
## PoblaDens    -0.023783621  1.054536e-03
## Urbano        0.002658964  1.282398e-02
## Tuberculosis -0.001454553 -4.717712e-05
## ImmunSaramp  -0.011947305  2.114653e-03
## BCG          -0.755972208  5.937696e-02
## Camas        -0.145093181 -3.615702e-02
## PBI           0.001168921  1.186925e-02
## TempMarzo    -0.023511127 -1.837927e-04

Cada una de las pendientes de un modelo de regresión lineal múltiple (coeficientes parciales de regresión de los predictores) se define del siguiente modo: Si el resto de variables se mantienen constantes, por cada unidad que aumenta el predictor en cuestión, la variable (Y) varía en promedio tantas unidades como indica la pendiente. Para este ejemplo, por cada unidad que aumenta el predictor PBI, las muertes logaritmicas por mil de covid aumenta en promedio 0.065 unidades, manteniéndose constantes el resto de predictores.

Modelo considerando muertes.permil

df_covid_muertes.permil = data.frame(Pobla80,PoblaMid, PoblaData, PoblaDens, Mujeres, Urbano,DisMort, Lesion,Tuberculosis,Diabetes, ImmunSaramp,HipTen.M, BCG,Medicos,Camas,PBI,TempMarzo,muertes.permil)

df_covid_muertes.permil
##       Pobla80 PoblaMid   PoblaData   PoblaDens  Mujeres  Urbano DisMort Lesion
## 1   0.2771541 54.32490  0.37172386  0.56937760 48.63585  25.495    29.8   19.5
## 2   2.7410163 68.58239  0.02866376  1.04612263 49.06309  60.319    17.0    4.0
## 3   1.2709846 63.48882  0.42228429  0.17730075 49.48427  72.629    14.2    9.5
## 4   0.2723907 50.97470  0.30809762  0.24713052 50.53046  65.514    16.5    9.2
## 5   2.6111754 64.12128  0.44494502  0.16258510 51.23735  91.870    15.8    6.5
## 6   3.1239347 68.11276  0.02951776  1.03680225 52.95658  63.149    22.3    3.9
## 7   4.0436339 65.15291  0.24992369  0.03249129 50.19962  86.012     9.1    5.9
## 8   5.2369767 66.70049  0.08847037  1.07206927 50.82943  58.297    11.4    5.2
## 9   1.3597424 70.43525  0.09942334  1.20265320 50.11575  55.680    22.2    4.6
## 10  0.9964088 67.13559  1.61356039 12.39579312 49.38730  36.632    21.6    7.5
## 11  3.8045048 68.28891  0.09485386  0.46728800 53.45605  78.595    23.7    7.0
## 12  5.6948380 64.15583  0.11422068  3.77214927 50.59332  98.001    11.4    6.4
## 13  1.0395255 64.98378  0.00383071  0.16793994 50.19252  45.724    22.1   13.2
## 14  0.4365651 54.29871  0.11485048  1.01853920 50.09820  47.312    19.6   10.2
## 15  1.2095158 68.22563  0.00754394  0.19777528 47.00264  40.895    23.3   10.5
## 16  1.6248620 61.73450  0.11353142  0.10480146 49.78340  69.425    17.2   13.1
## 17  3.3861895 68.76346  0.03323929  0.64920488 51.01054  48.245    17.8    3.7
## 18  0.4939442 61.66318  0.02254126  0.03977425 51.73076  69.446    20.3    8.3
## 19  1.8060280 69.74309  2.09469333  0.25061716 50.82992  86.569    16.6   12.2
## 20  0.7583938 72.10039  0.00428962  0.81396964 48.03618  77.629    16.6    7.4
## 21  4.7222857 64.38262  0.07024216  0.64703537 51.41409  75.008    23.6    2.6
## 22  0.2453268 52.64494  0.19751535  0.72191283 50.09548  29.358    21.7   11.0
## 23  0.2917947 52.25138  0.11175378  4.35178271 50.42199  13.032    22.9   12.1
## 24  0.5703887 64.22991  0.16249798  0.92056413 51.19798  23.388    21.1   10.0
## 25  0.3083562 54.63954  0.25216237  0.53343989 50.00349  56.374    21.6   10.9
## 26  4.3071336 66.89774  0.37058856  0.04075308 50.39153  81.411     9.8    6.1
## 27  0.3528256 52.87991  0.04666377  0.07490412 50.43647  41.364    23.1   10.3
## 28  1.7597254 68.44406  0.49648685  0.44748702 50.92577  80.778    15.8   15.0
## 29  0.3889863 57.45457  0.00832322  4.47244478 49.55870  28.965    22.9   10.9
## 30  0.2877278 55.55478  0.05244363  0.15356846 50.06560  66.916    16.7    9.9
## 31  5.5638202 65.04263  0.04089400  0.73077198 51.85262  56.947    16.7    5.3
## 32  4.0918014 64.99252  0.10625695  1.37602888 50.80859  73.792    15.0    4.7
## 33  4.5174188 63.72878  0.05797446  1.38067302 50.27420  87.874    11.3    3.8
## 34  0.6099089 65.89830  0.00958920  0.41368421 47.36679  77.777    19.6   10.4
## 35  1.5873319 64.94005  0.10627165  2.19978576 50.00780  81.074    19.0   12.0
## 36  1.5376442 64.81412  0.17084357  0.68788682 49.97063  63.821    13.0   12.8
## 37  0.7910340 60.97150  0.98423595  0.98873469 49.46997  42.704    27.7    5.8
## 38  1.8419292 64.58083  0.06420744  3.09881467 53.11400  72.023    14.0   15.4
## 39  0.2886712 60.42552  0.01308974  0.46665740 44.45585  72.143    22.0   10.7
## 40  5.5491132 64.01657  0.01320884  0.30386105 52.85843  68.880    17.0    4.5
## 41  0.5012502 55.71589  1.09224559  1.09224559 49.97889  20.763    18.3   11.7
## 42  0.5795601 65.03770  0.00883483  0.48357033 49.30050  56.248    30.6    5.4
## 43  5.4057724 62.13403  0.05518050  0.18156856 50.72076  85.382    10.2    5.5
## 44  6.1032258 62.00891  0.66987244  1.22338396 51.58424  80.444    10.6    6.4
## 45  0.5335561 59.41022  0.02119275  0.08224764 49.06862  89.370    14.4    9.1
## 46  0.2595557 53.14127  0.02280102  2.25306522 50.40213  61.270    20.4   10.9
## 47  3.6827447 65.33927  0.03731000  0.65275202 52.29124  58.632    24.9    3.6
## 48  6.6270801 64.91701  0.82927922  2.37370970 50.66037  77.312    12.1    4.0
## 49  0.3266130 59.33504  0.29767108  1.30821429 49.32583  56.060    20.8    9.8
## 50  7.2679122 64.27348  0.10727668  0.83224732 50.91620  79.058    12.4    2.9
## 51  1.0548473 60.75253  0.17247807  1.60953779 50.75935  51.054    14.9   15.7
## 52  0.3195615 53.22380  0.12414318  0.50522212 51.82111  36.140    22.4    9.2
## 53  1.2722362 65.33518  0.00779004  0.03957348 49.80802  26.606    30.5   12.4
## 54  0.8276328 61.80835  0.11123176  4.03598549 50.65221  55.278    26.5   12.6
## 55  1.0293956 63.56850  0.09587522  0.85687032 50.04934  57.096    14.0   19.6
## 56  4.2713096 66.43028  0.09768785  1.07906606 52.43243  71.351    23.0    4.4
## 57  0.3130915 50.39318  0.15477751  0.12291734 50.08490  23.059    23.9    9.3
## 58  2.7008155 68.71630  0.18729160  0.25189446 50.72703  87.564    12.4    7.1
## 59  1.7877871 71.20211 13.92730000  1.48348833 48.67937  59.152    17.0    7.0
## 60  0.9448065 66.76674 13.52617328  4.54938073 48.02354  34.030    23.3   11.3
## 61  0.8561815 67.59164  2.67663435  1.47752190 49.64388  55.325    26.4    6.0
## 62  1.1233084 69.33887  0.81800269  0.50222420 49.43913  74.898    14.8   10.1
## 63  0.4831038 58.28983  0.38433600  0.88530570 49.40868  70.473    21.3   28.4
## 64  3.0188525 64.72778  0.04853506  0.70452983 50.42551  63.170    10.3    4.3
## 65  3.0461867 60.09777  0.08883800  4.10526802 50.29813  92.418     9.6    4.1
## 66  7.1734357 63.91920  0.60431283  2.05450748 51.37667  70.438     9.5    3.8
## 67  2.0456797 67.45326  0.02934855  2.70993075 50.33983  55.674    14.7    8.7
## 68  8.3613475 59.72678  1.26529100  3.47073458 51.15926  91.616     8.4    4.8
## 69  0.6023762 61.90802  0.09956011  1.12142498 49.38968  90.979    19.2   10.9
## 70  1.4932044 64.14760  0.18276499  0.06769826 51.51148  57.428    26.8    9.5
## 71  0.2699139 57.87865  0.51393010  0.90299417 50.31602  27.030    13.4    9.6
## 72  0.2327419 75.91064  0.04137309  2.32172222 39.54817 100.000    17.4   12.8
## 73  5.5652960 63.96060  0.01926542  0.30983307 54.01017  68.142    21.9    5.4
## 74  1.4828557 66.90176  0.06848925  6.69494135 49.70581  88.593    17.9    5.8
## 75  0.8209008 62.38256  0.02108132  0.69437813 50.71690  28.153    26.6    8.3
## 76  0.3996440 55.62158  0.04818977  0.50030907 49.76829  51.151    17.6   10.0
## 77  0.7605248 67.28872  0.06678567  0.03795632 49.48379  80.102    20.1   20.1
## 78  5.8370281 65.41254  0.02789533  0.44531351 53.79196  67.679    20.7    6.6
## 79  4.0235270 69.93802  0.00607728  2.50093827 49.53926  90.981    10.0    6.7
## 80  0.4241700 56.34513  0.26262368  0.45139856 50.12397  37.191    22.9   10.6
## 81  0.3087382 53.45228  0.18143315  1.92440762 50.70152  16.937    16.4    8.6
## 82  1.0741699 69.33310  0.31528585  0.95962821 48.57852  76.036    17.2    8.9
## 83  0.2683715 49.94928  0.19077690  0.15635016 49.94100  42.356    24.6    8.9
## 84  0.4204185 56.77571  0.04403319  0.04272164 49.82266  53.672    18.1    9.4
## 85  2.0227961 70.73213  0.01265303  6.23301970 50.57711  40.793    22.6    4.8
## 86  1.5523039 66.21947  1.26190788  0.64914626 51.08928  80.156    15.7   10.3
## 87  2.2672524 72.67035  0.03545883  1.23519804 52.03556  42.629    24.9    5.7
## 88  0.6283914 65.50689  0.03170208  0.02040609 50.66954  68.445    30.2   10.6
## 89  3.4204855 66.81554  0.00622345  0.46271004 50.55901  66.813    20.6    3.5
## 90  1.1687236 65.78072  0.36029138  0.80728519 50.40385  62.453    12.4    6.4
## 91  0.3481273 52.43844  0.29495962  0.37508535 51.47519  35.988    18.4    7.8
## 92  0.7791075 67.84431  0.53708395  0.82238615 51.80790  30.579    24.2    8.6
## 93  0.6151794 59.45372  0.02448255  0.02973746 51.55369  50.032    21.3    9.8
## 94  0.7516195 63.85806  0.28087871  1.95939107 54.53534  19.740    21.8    8.8
## 95  4.6976392 64.69565  0.17231017  5.11457910 50.22094  91.490    11.2    5.2
## 96  3.7311137 64.69414  0.04885500  0.18554176 50.83777  86.538    10.1    6.0
## 97  1.1025688 64.55082  0.06465513  0.53727048 50.71271  58.522    14.2   12.7
## 98  0.2340882 47.42067  0.22442948  0.17717651 49.77100  16.425    20.0   10.4
## 99  4.2231531 65.40174  0.05314336  0.14554920 49.52463  82.248     9.2    5.6
## 100 0.4596337 75.36071  0.04829483  0.15604145 34.01408  84.539    17.8   17.7
## 101 0.6545620 60.41741  2.12215030  2.75289319 48.53807  36.666    24.7    7.3
## 102 1.9205337 64.83296  0.04176873  0.56186077 49.90538  67.709    13.0    9.7
## 103 1.2135437 64.12928  0.06956071  0.17508359 49.15161  61.585    17.5   12.0
## 104 1.6809015 66.12100  0.31989256  0.24991606 50.33776  77.907    12.6   10.5
## 105 0.7893793 63.91439  1.06651922  3.57688305 49.74166  46.907    26.8    7.5
## 106 4.4631164 67.42991  0.37978548  1.24035886 51.53071  60.058    18.7    4.7
## 107 6.3408699 64.58823  0.10281762  1.12239454 52.71196  65.211    11.1    4.2
## 108 0.1847213 85.08917  0.02781677  2.39593196 24.49529  99.135    15.3   25.9
## 109 4.7084438 66.12674  0.19473936  0.84639847 51.34374  53.998    21.4    3.6
## 110 0.3624706 57.08623  0.12301939  4.98659870 50.86106  17.211    18.2   13.5
## 111 2.4482249 67.86891  0.00110210  2.82589744 49.20878  52.198    23.2    5.6
## 112 0.5038453 71.64306  0.33699947  0.15676654 42.44585  83.844    16.4   16.3
## 113 0.3825330 53.85775  0.15854360  0.82347478 51.27733  47.192    18.1   12.2
## 114 4.0191136 65.96453  0.06982084  0.79831740 51.00252  56.092    19.1    3.0
## 115 0.3522140 55.97380  0.07650154  1.05987171 50.12127  42.055    30.5    8.9
## 116 2.1766595 76.25834  0.05638676 79.52998418 47.65813 100.000     9.3    3.7
## 117 3.2223629 68.92462  0.05447011  1.13290578 51.33833  53.726    17.2    6.0
## 118 5.3152643 65.37135  0.02067372  1.02639860 50.24521  54.541    12.7    6.6
## 119 0.7184723 65.60251  0.57779622  0.47630120 50.69415  66.355    26.2    9.1
## 120 3.2120425 72.60812  0.51635256  5.29652104 49.91688  81.459     7.8   10.0
## 121 6.1672057 65.95449  0.46723749  0.93529058 50.89664  80.321     9.9    3.5
## 122 1.6065781 65.32978  0.21670000  3.45558922 51.96682  18.476    17.4    9.7
## 123 5.1726032 62.32269  0.10183175  0.25001043 49.94578  87.431     9.1    4.9
## 124 5.1493834 66.46583  0.08516543  2.15521378 50.42712  73.797     8.6    6.1
## 125 0.5234433 60.19383  0.09100837  0.65572714 49.58384  27.134    25.3    7.6
## 126 2.5315654 71.01212  0.69428524  1.35897207 51.26870  49.949    14.5   10.2
## 127 0.2700138 55.79601  0.07889094  1.45046773 50.26805  41.702    23.6   10.7
## 128 1.7197442 67.51420  0.11565204  0.74441323 50.43715  68.945    16.1    6.4
## 129 1.6774797 66.86738  0.82319724  1.06960129 50.67781  75.143    16.1    6.2
## 130 0.2057545 51.12849  0.42723139  2.13061734 50.77608  23.774    21.9   12.7
## 131 3.9876216 67.75290  0.44622516  0.77029667 53.68775  69.352    24.7    5.0
## 132 0.1307769 84.31149  0.09630959  1.35609110 30.63669  86.522    16.8   16.8
## 133 5.0282090 63.92605  0.66488991  2.74827392 50.63527  83.398    10.9    3.5
## 134 3.8751036 65.48331  3.27167434  0.35766089 50.52001  82.256    14.6    6.6
## 135 4.3947641 64.57750  0.03449299  0.19708028 51.72154  95.334    16.7    7.5
## 136 0.8369038 66.89480  0.32955400  0.77469205 50.13736  50.478    24.5    6.0
## 137 0.3498816 57.50884  0.28498687  0.53977853 49.61166  36.642    30.6   14.7
## 138 0.2529708 52.96418  0.17351822  0.23341479 50.49321  43.521    17.9   10.2
## 139 0.4222739 54.65941  0.14439018  0.37324591 52.35675  32.209    19.3   12.3
##     Tuberculosis Diabetes ImmunSaramp HipTen.M BCG    Medicos      Camas
## 1          189.0      9.2          64     19.8   1 0.24009091  0.4363636
## 2           18.0      9.0          94     39.4   1 1.21237143  2.9375000
## 3           69.0      6.7          80     23.0   1 1.31202500  1.9000000
## 4          355.0      4.5          50     25.2   1 0.17300000  0.8000000
## 5           27.0      5.9          94     32.9   1 3.57165000  4.6000000
## 6           31.0      6.1          95     43.4   1 3.06122500  4.0200000
## 7            6.6      5.6          95     32.8   1 3.27402222  3.8783333
## 8            7.1      6.6          94     38.8   1 4.66801111  7.7000000
## 9           63.0      6.1          96     31.4   1 3.58736250  6.4666667
## 10         221.0      9.2          97     17.4   1 0.39047692  0.5750000
## 11          31.0      5.0          97     45.5   1 4.22154000 11.2000000
## 12           9.0      4.6          96     35.0   0 2.79814444  6.5500000
## 13          30.0     17.1          97     25.1   1 0.96785000  1.1600000
## 14          56.0      1.0          71     40.3   1 0.11874000  0.5000000
## 15         149.0     10.3          97     20.4   1 0.26431111  1.7333333
## 16         108.0      6.8          89     27.2   1 0.71612500  1.1000000
## 17          25.0      9.0          68     43.2   1 1.77503333  3.3666667
## 18         275.0      5.8          97     41.0   1 0.35774444  2.0000000
## 19          45.0     10.4          84     38.5   1 1.84654000  2.3285714
## 20          68.0     13.3          99     32.0   1 1.38302727  2.7542857
## 21          22.0      6.0          93     46.5   1 3.80887500  6.4888889
## 22          48.0      7.3          88     12.4   1 0.04237143  0.6500000
## 23         111.0      5.1          88     30.8   1 0.04885000  1.1333333
## 24         302.0      6.4          84     24.1   1 0.24971429  0.7600000
## 25         186.0      6.0          71     17.9   1 0.07534000  1.4000000
## 26           5.6      7.6          90     23.9   0 2.32944444  3.0714286
## 27         540.0      6.0          49     33.2   1 0.04973333  1.1000000
## 28          33.0      7.4          95     28.7   1 1.70129231  1.3166667
## 29          35.0     12.3          90     41.5   1 0.18323333  2.2000000
## 30         375.0      6.0          75     19.3   1 0.12880000  1.6000000
## 31           8.4      5.4          93     38.5   1 2.80697000  5.6354545
## 32           5.4      7.0          96     42.6   1 3.70292500  7.0100000
## 33           5.4      8.3          95     34.6   1 3.56958333  3.4111111
## 34         260.0      5.1          86     22.3   1 0.21512500  1.4571429
## 35          45.0      8.6          95     29.3   1 1.37733333  1.4375000
## 36          44.0      5.5          83     28.6   1 1.90360000  1.5285714
## 37          12.0     17.2          94     23.9   1 1.73048750  1.5100000
## 38          70.0      8.8          81     30.0   1 1.65428000  0.9900000
## 39         201.0      6.0          30     25.2   1 0.40000000  2.0666667
## 40          13.0      4.2          87     42.9   1 3.34070000  5.3600000
## 41         151.0      4.3          61     22.2   1 0.03620000  1.7500000
## 42          54.0     14.7          94     25.0   1 0.58042000  2.1475000
## 43           4.7      5.6          96     46.6   1 3.14394000  5.9600000
## 44           8.9      4.8          90     38.5   1 3.26508571  6.9222222
## 45         525.0      6.0          59     43.6   1 0.36110000  3.2000000
## 46         174.0      1.9          91     30.4   1 0.09858333  1.0000000
## 47          80.0      5.8          98     41.0   1 4.62520000  3.1222222
## 48           7.3     10.4          97     34.2   1 3.84022000  8.2555556
## 49         148.0      2.5          92     41.4   1 0.12015714  0.9000000
## 50           4.5      4.7          97     36.4   1 5.71011111  4.6555556
## 51          26.0     10.0          87     26.5   1 0.62945000  0.6250000
## 52         176.0      2.4          48     41.0   1 0.09196667  0.3000000
## 53          83.0     11.6          98     26.3   1 0.56823333  2.1600000
## 54         176.0      6.7          69     26.7   1 0.18585000  1.0000000
## 55          37.0      7.3          89     25.7   1 0.57160000  0.7285714
## 56           6.4      6.9          99     47.4   1 3.24861667  7.3000000
## 57         142.0      6.0          37     31.3   1 0.04200000  0.4000000
## 58          18.0      8.6          93     28.2   1 1.03645000  2.1714286
## 59          61.0      9.2          99     32.3   1 1.57320000  3.4066667
## 60         199.0     10.4          90     29.2   1 0.67390833  0.8000000
## 61         316.0      6.3          75     24.7   1 0.24655000  0.9000000
## 62          14.0      9.6          99     25.9   1 0.98516000  1.4400000
## 63          42.0      8.8          83     23.9   1 0.72533333  1.3100000
## 64           7.0      3.2          92     32.2   1 2.81283000  3.9555556
## 65           4.0      9.7          98     40.1   1 3.38105556  3.5333333
## 66           7.0      5.0          93     36.5   0 3.94927778  3.6875000
## 67           2.9     11.3          89     33.6   1 0.54227143  1.7571429
## 68          14.0      5.6          97     37.5   1 2.27806667 13.7960000
## 69           5.0     12.7          92     26.3   1 2.24923636  1.7818182
## 70          68.0      6.1          99     33.1   1 3.60587000  7.4111111
## 71         292.0      3.1          89     36.8   1 0.18122857  1.4000000
## 72          23.0     12.2          99     26.5   1 2.16873636  1.9800000
## 73          29.0      5.0          98     36.7   1 3.38225000  6.8111111
## 74          11.0     11.2          82     29.6   0 2.55344444  3.3800000
## 75         611.0      4.5          90     35.0   1 0.06760000  1.3000000
## 76         308.0      2.4          91     40.3   1 0.02466667  0.7500000
## 77          40.0     10.2          97     23.6   1 1.98748333  3.6700000
## 78          44.0      3.8          92     40.5   1 4.08613000  7.1666667
## 79           8.0      5.0          99     37.1   1 2.85313000  5.3727273
## 80         233.0      4.5          62     32.2   1 0.17360000  0.2500000
## 81         181.0      4.5          87     32.6   1 0.01767500  1.2000000
## 82          92.0     16.7          96     25.2   1 1.24358000  1.8312500
## 83          53.0      2.4          70     32.1   1 0.09575714  0.3333333
## 84          93.0      7.1          78     29.5   1 0.14066667  0.4000000
## 85          13.0     22.0          99     48.4   1 1.45775556  3.2333333
## 86          23.0     13.5          97     28.5   1 2.03156667  1.5888889
## 87          86.0      5.7          93     42.2   1 2.62560000  6.1666667
## 88         428.0      4.7          99     30.6   1 2.96358889  6.3266667
## 89          15.0      9.0          58     41.9   1 2.08927778  4.0125000
## 90          99.0      7.0          99     33.0   1 0.63655000  0.9800000
## 91         551.0      3.3          85     31.5   1 0.04688750  0.7666667
## 92         338.0      3.9          93     25.5   1 0.53712000  0.7500000
## 93         524.0      4.5          82     41.9   1 0.37305000  3.0000000
## 94         151.0      7.2          91     22.1   1 0.57297500  2.6500000
## 95           5.3      5.4          93     33.8   0 3.40828889  4.6000000
## 96           7.3      6.2          92     30.9   1 2.70726000  2.5500000
## 97          41.0     11.4          99     26.2   1 0.74390000  0.9222222
## 98          87.0      2.4          77     32.4   1 0.03340000  0.3000000
## 99           4.1      5.3          96     33.0   1 4.21626667  4.2555556
## 100          5.9     10.1          99     26.2   1 1.95183077  1.8600000
## 101        265.0     19.9          76     21.1   1 0.84727500  0.7200000
## 102         52.0      7.7          98     24.6   1 1.43397000  2.2888889
## 103         43.0      9.6          93     27.2   1 0.96393333  1.2857143
## 104        123.0      6.6          85     17.8   1 1.18280000  1.4875000
## 105        554.0      7.1          67     23.5   1 1.25186667  0.5833333
## 106         16.0      6.1          93     35.0   1 2.19399000  6.5222222
## 107         24.0      9.8          99     35.7   1 3.79097273  3.4222222
## 108         31.0     15.6          99     36.2   1 2.47302500  1.6400000
## 109         68.0      6.9          90     46.7   1 2.40146667  6.4555556
## 110         59.0      5.1          99     30.4   1 0.09168889  1.6000000
## 111          6.3     11.6          99     30.7   1 0.65870000  3.3000000
## 112         10.0     15.8          98     27.0   1 2.25626250  2.2500000
## 113        118.0      2.4          82     40.4   1 0.14020000  0.2000000
## 114         17.0      9.0          92     41.6   1 2.42901111  5.6125000
## 115        298.0      2.4          80     39.7   1 0.02050000  0.4000000
## 116         47.0      5.5          95     22.1   1 1.76859091  2.7457143
## 117          5.8      6.5          96     33.7   1 3.11790000  6.3400000
## 118          5.3      5.9          93     37.2   1 2.59476000  4.6555556
## 119        520.0     12.7          70     45.9   1 0.76988750  2.8000000
## 120         66.0      6.9          98     23.5   1 2.05984615 10.6080000
## 121          9.4      6.9          97     37.1   1 4.15920000  3.1888889
## 122         64.0     10.7          99     25.8   1 0.77147000  3.5500000
## 123          5.5      4.8          97     37.0   0 4.03310000  2.7666667
## 124          6.4      5.7          96     31.7   1 4.00455000  5.1222222
## 125         84.0      6.1          98     25.4   1 1.78443333  5.2666667
## 126        153.0      7.0          96     23.8   1 0.40473333  2.1000000
## 127         36.0      2.4          85     40.1   1 0.08897500  0.8000000
## 128         35.0      8.5          96     25.2   1 1.16097143  2.0545455
## 129         16.0     11.1          96     35.1   1 1.65210000  2.4555556
## 130        200.0      2.5          86     32.7   1 0.10457500  0.7500000
## 131         80.0      6.1          91     50.6   1 3.35196667  8.9222222
## 132          1.0     16.3          99     19.2   1 1.70489167  1.6777778
## 133          8.0      3.9          92     29.9   1 2.75287500  3.2111111
## 134          3.0     10.8          92     31.8   0 2.52747500  3.0333333
## 135         33.0      7.3          97     38.8   1 4.16766000  2.5250000
## 136         70.0      6.5          96     27.7   1 2.50781250  4.5888889
## 137         48.0      5.4          64     12.4   1 0.30986667  0.6900000
## 138        346.0      4.5          94     27.4   1 0.08791250  1.9500000
## 139        210.0      1.8          88     32.0   1 0.06561250  2.3500000
##             PBI TempMarzo muertes.permil
## 1     1.8351696      7.60     6.10668360
## 2    11.3351950      6.04    11.51279525
## 3    14.1967389     17.91    14.75309441
## 4     6.7205961     22.78     0.12982898
## 5    20.0684923     17.51    11.23734344
## 6     8.3491802     -0.57    33.20035125
## 7    45.7525548     25.37     4.12125797
## 8    48.9687140      1.42    72.90576495
## 9    17.0906963      4.97     5.43132025
## 10    3.3061083     25.42     3.37142634
## 11   18.1721809     -0.69    22.56102177
## 12   45.2631622      5.23   819.81651659
## 13    8.0937796     24.45     5.22096426
## 14    2.0675705     30.14     0.26120918
## 15    8.3417246      5.68     0.00000000
## 16    6.5317860     22.07    24.66277617
## 17   11.6971771      4.01    45.42816649
## 18   16.1336867     24.30     0.44363092
## 19   15.5847506     25.49   122.20404597
## 20   80.8004129     25.92     4.66241765
## 21   17.9465777      4.70    18.93449746
## 22    1.6709928     30.63     2.68333575
## 23    0.7568378     20.43     0.08948243
## 24    3.3333517     27.93     0.00000000
## 25    3.2930885     26.27     7.01928682
## 26   44.2264907    -18.72   182.54745910
## 27    0.8532664     26.96     0.21429902
## 28   13.2116325     25.14    16.17364085
## 29    2.6420144     25.20     2.40291618
## 30    5.6636489     25.60     3.62293762
## 31   22.9922117      5.83    24.69799971
## 32   32.7718057      2.80    29.83334267
## 33   48.5249925      2.24    97.45670766
## 34    2.7442687     25.75    18.77111751
## 35   13.9050879     22.88    44.60267625
## 36   10.8759023     21.93   191.69583028
## 37   10.8110341     17.83     8.29069493
## 38    7.2348467     25.70     6.07406245
## 39   30.5908474     25.04     9.16748537
## 40   28.8340001     -2.35    49.96653756
## 41    1.5126247     23.48     0.05493270
## 42    8.8754441     25.00     0.00000000
## 43   42.8552430     -6.09    56.72293654
## 44   40.3515680      6.37   426.88724438
## 45   17.0778163     26.14     6.60603272
## 46    2.4051919     27.32     0.43857687
## 47    9.5834608      0.72     3.21629590
## 48   46.5762068      3.87   101.42542822
## 49    3.9219764     29.52     1.14220031
## 50   27.2065489      8.19    16.12652442
## 51    7.5159291     22.94     3.94253020
## 52    2.0022876     27.63     1.69159514
## 53    7.1987245     25.65    14.12059502
## 54    1.7058976     23.47     3.05668093
## 55    4.4336960     23.38    20.23463414
## 56   25.7573736      5.44    52.10473974
## 57    2.0073216     26.55     4.13496767
## 58   22.2523960     10.87    44.90324179
## 59   13.5313781      0.49     3.33015014
## 60    5.8366566     23.45     3.34980183
## 61   10.5772045     25.79     5.50317977
## 62   18.4501839     11.33    92.46913357
## 63   15.8895142     15.12     4.55330752
## 64   59.3055780      6.00   336.04573683
## 65   34.5710854     14.96    31.63060852
## 66   37.7630816      6.52   547.26622303
## 67    8.5085469     23.44     3.06659102
## 68   39.1530060      2.52     6.78104879
## 69    9.2014965     13.08     0.90397650
## 70   24.1030338     -3.96     2.02445775
## 71    2.8839337     26.10     1.07018445
## 72   75.8307296     19.23    42.29802512
## 73   23.8146826     -1.37    11.93848875
## 74   13.2450688     10.39     3.79621619
## 75    2.9043623     15.13     0.00000000
## 76    1.2563111     26.44     5.60284890
## 77   19.3396159     17.76     0.59893088
## 78   27.8087523     -0.47    23.65987425
## 79  100.2191161      4.70   181.00202722
## 80    1.7044431     23.99     0.07615460
## 81    1.1836484     23.25     0.22046688
## 82   25.8714034     25.19     3.64748370
## 83    2.0131236     27.34     3.66920733
## 84    3.8172107     25.29     3.63362273
## 85   19.3987280     25.48     7.90324531
## 86   17.8555895     18.44    68.12700147
## 87    5.8933274      2.93    77.27271317
## 88   11.1277975     -8.80     0.00000000
## 89   16.2734892      2.53    14.46143216
## 90    7.4795258     13.09     5.60657321
## 91    1.2616857     25.48     0.03390295
## 92    5.0214146     22.74     0.11171438
## 93   10.2230967     22.67     0.00000000
## 94    2.4752041     10.02     0.14241022
## 95   49.9843155      4.95   340.72277916
## 96   36.4995043     13.60     4.50312148
## 97    4.8837325     24.95     5.41333688
## 98    0.9278714     26.01     2.85167528
## 99   62.6503184     -5.49    44.22001168
## 100  42.4790524     23.40     7.86833705
## 101   4.6501098     16.01     5.93737399
## 102  20.8839055     25.44    75.41526879
## 103  11.4029323     25.84     1.58135246
## 104  12.3515166     19.99   124.51055442
## 105   7.0082665     25.13     8.47617167
## 106  25.9904310      2.65    27.06791213
## 107  29.3390041     11.33   131.88400976
## 108 123.2139364     21.78    10.78486107
## 109  21.6182719      3.40    62.59648794
## 110   1.7889788     19.25     0.00000000
## 111  10.9317631     25.90     0.00000000
## 112  51.5878305     20.60    12.61129580
## 113   3.1310271     28.47     2.45989116
## 114  14.9080484      4.91    34.37369129
## 115   1.4959394     27.65     5.88223453
## 116  86.0684237     28.62     4.07897173
## 117  29.0915206      2.40     5.14043390
## 118  31.7402860      3.38    51.75652955
## 119  12.8666891     21.10     9.55354121
## 120  34.6370853      3.66     5.20961879
## 121  34.5888453      8.60   580.39007101
## 122  11.1185327     27.03     0.46146747
## 123  47.6285919     -4.98   414.40906201
## 124  61.3146089      0.09   193.38832670
## 125   2.7293337     -3.08     5.05448015
## 126  15.8571484     27.38     0.82098822
## 127   1.4956789     29.31     1.64784448
## 128  11.2660561     14.21     4.15038075
## 129  23.5212137      4.77    53.41368734
## 130   1.8103287     23.68     0.00000000
## 131   8.4417522      1.18    14.43217590
## 132  65.5180899     22.61    26.47711407
## 133  41.1611271      4.66   563.40154117
## 134  55.0581658      0.06   307.00488362
## 135  20.4797528     21.26     6.37810755
## 136   6.8361061      5.38     0.42481657
## 137   3.5314239     20.82     1.85973480
## 138   3.8025013     22.90     0.40341585
## 139   2.5606953     22.92     0.27702715
modelo_muertes.permil<-lm(muertes.permil~.,data = df_covid_muertes.permil)
summary(modelo_muertes.permil)
## 
## Call:
## lm(formula = muertes.permil ~ ., data = df_covid_muertes.permil)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -215.02  -26.08   -7.73   11.31  429.15 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.910e+02  2.749e+02   0.695  0.48849    
## Pobla80       2.962e+01  9.338e+00   3.172  0.00192 ** 
## PoblaMid     -2.665e-01  2.160e+00  -0.123  0.90202    
## PoblaData     1.687e+00  4.801e+00   0.351  0.72590    
## PoblaDens    -1.911e+00  1.323e+00  -1.445  0.15111    
## Mujeres       1.955e+00  3.920e+00   0.499  0.61884    
## Urbano        2.183e-02  5.847e-01   0.037  0.97029    
## DisMort      -6.788e-01  2.236e+00  -0.304  0.76200    
## Lesion       -1.842e-01  2.676e+00  -0.069  0.94525    
## Tuberculosis  9.083e-03  7.387e-02   0.123  0.90234    
## Diabetes     -4.035e+00  2.714e+00  -1.487  0.13972    
## ImmunSaramp   2.805e-01  7.732e-01   0.363  0.71744    
## HipTen.M     -9.491e-01  1.228e+00  -0.773  0.44095    
## BCG          -2.423e+02  4.055e+01  -5.975 2.38e-08 ***
## Medicos      -3.388e+00  1.285e+01  -0.264  0.79256    
## Camas        -1.097e+01  5.586e+00  -1.963  0.05191 .  
## PBI           1.470e+00  7.395e-01   1.988  0.04904 *  
## TempMarzo     4.740e-01  1.269e+00   0.374  0.70936    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 90.85 on 121 degrees of freedom
## Multiple R-squared:  0.5449, Adjusted R-squared:  0.481 
## F-statistic: 8.524 on 17 and 121 DF,  p-value: 6.558e-14

El modelo con todas las variables introducidas como predictores tiene un \(R^2\) alta (0.5449), es capaz de explicar el 55% de la variabilidad observada en la muertes logarimicas por mil de covid. El p-value del modelo es significativo (6.55e-13) por lo que se puede aceptar que el modelo no es por azar, al menos uno de los coeficientes parciales de regresión es distinto de 0. Muchos de ellos no son significativos, lo que es un indicativo de que podrían no contribuir al modelo.

step(object = modelo_muertes.permil, direction = "both", trace = 1)
## Start:  AIC=1270.28
## muertes.permil ~ Pobla80 + PoblaMid + PoblaData + PoblaDens + 
##     Mujeres + Urbano + DisMort + Lesion + Tuberculosis + Diabetes + 
##     ImmunSaramp + HipTen.M + BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - Urbano        1        11  998704 1268.3
## - Lesion        1        39  998732 1268.3
## - Tuberculosis  1       125  998817 1268.3
## - PoblaMid      1       126  998818 1268.3
## - Medicos       1       573  999266 1268.4
## - DisMort       1       760  999453 1268.4
## - PoblaData     1      1019  999712 1268.4
## - ImmunSaramp   1      1086  999779 1268.4
## - TempMarzo     1      1152  999845 1268.4
## - Mujeres       1      2053 1000746 1268.6
## - HipTen.M      1      4933 1003626 1269.0
## <none>                       998693 1270.3
## - PoblaDens     1     17228 1015921 1270.7
## - Diabetes      1     18241 1016934 1270.8
## - Camas         1     31814 1030506 1272.6
## - PBI           1     32627 1031319 1272.8
## - Pobla80       1     83040 1081732 1279.4
## - BCG           1    294672 1293364 1304.2
## 
## Step:  AIC=1268.28
## muertes.permil ~ Pobla80 + PoblaMid + PoblaData + PoblaDens + 
##     Mujeres + DisMort + Lesion + Tuberculosis + Diabetes + ImmunSaramp + 
##     HipTen.M + BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - Lesion        1        35  998739 1266.3
## - PoblaMid      1       114  998818 1266.3
## - Tuberculosis  1       130  998834 1266.3
## - Medicos       1       591  999295 1266.4
## - DisMort       1       965  999669 1266.4
## - PoblaData     1      1009  999713 1266.4
## - ImmunSaramp   1      1083  999787 1266.4
## - TempMarzo     1      1217  999921 1266.5
## - Mujeres       1      2058 1000762 1266.6
## - HipTen.M      1      4922 1003626 1267.0
## <none>                       998704 1268.3
## - PoblaDens     1     17240 1015944 1268.7
## - Diabetes      1     18236 1016940 1268.8
## + Urbano        1        11  998693 1270.3
## - Camas         1     32001 1030705 1270.7
## - PBI           1     33273 1031977 1270.8
## - Pobla80       1     83637 1082341 1277.5
## - BCG           1    304782 1303486 1303.3
## 
## Step:  AIC=1266.29
## muertes.permil ~ Pobla80 + PoblaMid + PoblaData + PoblaDens + 
##     Mujeres + DisMort + Tuberculosis + Diabetes + ImmunSaramp + 
##     HipTen.M + BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - PoblaMid      1       106  998845 1264.3
## - Tuberculosis  1       156  998895 1264.3
## - Medicos       1       580  999319 1264.4
## - DisMort       1       954  999693 1264.4
## - PoblaData     1      1018  999757 1264.4
## - ImmunSaramp   1      1068  999807 1264.4
## - TempMarzo     1      1186  999925 1264.5
## - Mujeres       1      2421 1001160 1264.6
## - HipTen.M      1      4982 1003721 1265.0
## <none>                       998739 1266.3
## - PoblaDens     1     17714 1016453 1266.7
## - Diabetes      1     18210 1016949 1266.8
## + Lesion        1        35  998704 1268.3
## + Urbano        1         7  998732 1268.3
## - Camas         1     32259 1030998 1268.7
## - PBI           1     33269 1032008 1268.8
## - Pobla80       1     90262 1089001 1276.3
## - BCG           1    305105 1303844 1301.3
## 
## Step:  AIC=1264.3
## muertes.permil ~ Pobla80 + PoblaData + PoblaDens + Mujeres + 
##     DisMort + Tuberculosis + Diabetes + ImmunSaramp + HipTen.M + 
##     BCG + Medicos + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - Tuberculosis  1       138  998983 1262.3
## - Medicos       1       678  999523 1262.4
## - PoblaData     1       959  999804 1262.4
## - DisMort       1       960  999805 1262.4
## - ImmunSaramp   1       962  999807 1262.4
## - TempMarzo     1      1261 1000106 1262.5
## - Mujeres       1      2973 1001818 1262.7
## - HipTen.M      1      4942 1003787 1263.0
## <none>                       998845 1264.3
## - PoblaDens     1     19045 1017890 1264.9
## - Diabetes      1     23371 1022216 1265.5
## + PoblaMid      1       106  998739 1266.3
## + Lesion        1        27  998818 1266.3
## + Urbano        1         0  998845 1266.3
## - Camas         1     33023 1031868 1266.8
## - PBI           1     33227 1032072 1266.8
## - Pobla80       1     90217 1089062 1274.3
## - BCG           1    308747 1307592 1299.7
## 
## Step:  AIC=1262.32
## muertes.permil ~ Pobla80 + PoblaData + PoblaDens + Mujeres + 
##     DisMort + Diabetes + ImmunSaramp + HipTen.M + BCG + Medicos + 
##     Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - Medicos       1       788  999771 1260.4
## - ImmunSaramp   1       859  999842 1260.4
## - DisMort       1       870  999854 1260.4
## - PoblaData     1      1030 1000014 1260.5
## - TempMarzo     1      1307 1000291 1260.5
## - Mujeres       1      3117 1002101 1260.8
## - HipTen.M      1      4846 1003829 1261.0
## <none>                       998983 1262.3
## - PoblaDens     1     19207 1018191 1263.0
## - Diabetes      1     25486 1024469 1263.8
## + Tuberculosis  1       138  998845 1264.3
## + PoblaMid      1        89  998895 1264.3
## + Lesion        1        49  998935 1264.3
## + Urbano        1         1  998983 1264.3
## - Camas         1     32885 1031869 1264.8
## - PBI           1     33719 1032703 1264.9
## - Pobla80       1     91392 1090376 1272.5
## - BCG           1    308931 1307914 1297.8
## 
## Step:  AIC=1260.43
## muertes.permil ~ Pobla80 + PoblaData + PoblaDens + Mujeres + 
##     DisMort + Diabetes + ImmunSaramp + HipTen.M + BCG + Camas + 
##     PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - ImmunSaramp   1       580 1000351 1258.5
## - DisMort       1      1098 1000869 1258.6
## - PoblaData     1      1202 1000974 1258.6
## - TempMarzo     1      2274 1002045 1258.8
## - Mujeres       1      3163 1002934 1258.9
## - HipTen.M      1      5297 1005068 1259.2
## <none>                       999771 1260.4
## - PoblaDens     1     18833 1018605 1261.0
## - Diabetes      1     26348 1026119 1262.0
## + Medicos       1       788  998983 1262.3
## + Tuberculosis  1       248  999523 1262.4
## + PoblaMid      1       182  999589 1262.4
## + Urbano        1        93  999678 1262.4
## + Lesion        1        37  999734 1262.4
## - PBI           1     33286 1033057 1263.0
## - Camas         1     33447 1033219 1263.0
## - Pobla80       1    105611 1105382 1272.4
## - BCG           1    311890 1311662 1296.2
## 
## Step:  AIC=1258.51
## muertes.permil ~ Pobla80 + PoblaData + PoblaDens + Mujeres + 
##     DisMort + Diabetes + HipTen.M + BCG + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - PoblaData     1      1248 1001599 1256.7
## - DisMort       1      1427 1001777 1256.7
## - TempMarzo     1      2054 1002405 1256.8
## - Mujeres       1      3220 1003571 1257.0
## - HipTen.M      1      5380 1005731 1257.3
## <none>                      1000351 1258.5
## - PoblaDens     1     18552 1018903 1259.1
## - Diabetes      1     26218 1026569 1260.1
## + ImmunSaramp   1       580  999771 1260.4
## + Medicos       1       508  999842 1260.4
## + Urbano        1       112 1000239 1260.5
## + Tuberculosis  1       100 1000251 1260.5
## + Lesion        1        25 1000326 1260.5
## + PoblaMid      1        19 1000332 1260.5
## - Camas         1     32874 1033224 1261.0
## - PBI           1     33672 1034023 1261.1
## - Pobla80       1    105663 1106014 1270.5
## - BCG           1    311622 1311973 1294.2
## 
## Step:  AIC=1256.69
## muertes.permil ~ Pobla80 + PoblaDens + Mujeres + DisMort + Diabetes + 
##     HipTen.M + BCG + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - DisMort       1      1527 1003126 1254.9
## - TempMarzo     1      1698 1003296 1254.9
## - Mujeres       1      2939 1004537 1255.1
## - HipTen.M      1      5453 1007051 1255.4
## <none>                      1001599 1256.7
## - PoblaDens     1     17889 1019488 1257.2
## - Diabetes      1     25201 1026799 1258.1
## + PoblaData     1      1248 1000351 1258.5
## + Medicos       1       640 1000958 1258.6
## + ImmunSaramp   1       625 1000974 1258.6
## + Tuberculosis  1       172 1001427 1258.7
## + Urbano        1       166 1001432 1258.7
## + Lesion        1        41 1001558 1258.7
## + PoblaMid      1         0 1001598 1258.7
## - PBI           1     32560 1034159 1259.1
## - Camas         1     33756 1035355 1259.3
## - Pobla80       1    106140 1107738 1268.7
## - BCG           1    311961 1313560 1292.4
## 
## Step:  AIC=1254.9
## muertes.permil ~ Pobla80 + PoblaDens + Mujeres + Diabetes + HipTen.M + 
##     BCG + Camas + PBI + TempMarzo
## 
##                Df Sum of Sq     RSS    AIC
## - TempMarzo     1      2517 1005643 1253.2
## - Mujeres       1      3537 1006663 1253.4
## - HipTen.M      1      6797 1009923 1253.8
## <none>                      1003126 1254.9
## - PoblaDens     1     18597 1021723 1255.5
## + DisMort       1      1527 1001599 1256.7
## + PoblaData     1      1349 1001777 1256.7
## - Diabetes      1     28230 1031356 1256.8
## + ImmunSaramp   1       980 1002146 1256.8
## + Medicos       1       821 1002305 1256.8
## + Tuberculosis  1        39 1003087 1256.9
## + Lesion        1        13 1003113 1256.9
## + Urbano        1         1 1003125 1256.9
## + PoblaMid      1         0 1003126 1256.9
## - Camas         1     37354 1040480 1258.0
## - PBI           1     47374 1050500 1259.3
## - Pobla80       1    139613 1142739 1271.0
## - BCG           1    316035 1319161 1291.0
## 
## Step:  AIC=1253.25
## muertes.permil ~ Pobla80 + PoblaDens + Mujeres + Diabetes + HipTen.M + 
##     BCG + Camas + PBI
## 
##                Df Sum of Sq     RSS    AIC
## - Mujeres       1      3106 1008749 1251.7
## - HipTen.M      1      6220 1011863 1252.1
## <none>                      1005643 1253.2
## - PoblaDens     1     16481 1022124 1253.5
## + TempMarzo     1      2517 1003126 1254.9
## + DisMort       1      2347 1003296 1254.9
## + Medicos       1      1967 1003677 1255.0
## - Diabetes      1     28136 1033780 1255.1
## + PoblaData     1       923 1004720 1255.1
## + ImmunSaramp   1       741 1004903 1255.1
## + Tuberculosis  1       105 1005538 1255.2
## + PoblaMid      1        74 1005570 1255.2
## + Lesion        1        15 1005628 1255.2
## + Urbano        1        14 1005630 1255.2
## - PBI           1     45714 1051357 1257.4
## - Camas         1     61792 1067435 1259.5
## - Pobla80       1    137573 1143216 1269.1
## - BCG           1    323740 1329383 1290.0
## 
## Step:  AIC=1251.67
## muertes.permil ~ Pobla80 + PoblaDens + Diabetes + HipTen.M + 
##     BCG + Camas + PBI
## 
##                Df Sum of Sq     RSS    AIC
## - HipTen.M      1      5691 1014440 1250.5
## - PoblaDens     1     14251 1023000 1251.6
## <none>                      1008749 1251.7
## + Mujeres       1      3106 1005643 1253.2
## + DisMort       1      2921 1005828 1253.3
## + TempMarzo     1      2086 1006663 1253.4
## + Medicos       1      1901 1006848 1253.4
## + ImmunSaramp   1       884 1007865 1253.5
## + PoblaData     1       720 1008029 1253.6
## + PoblaMid      1       495 1008254 1253.6
## + Tuberculosis  1       172 1008577 1253.7
## + Lesion        1       157 1008592 1253.7
## + Urbano        1         1 1008748 1253.7
## - Diabetes      1     34542 1043291 1254.3
## - Camas         1     59821 1068570 1257.7
## - PBI           1     61058 1069807 1257.8
## - Pobla80       1    201816 1210565 1275.0
## - BCG           1    329319 1338068 1288.9
## 
## Step:  AIC=1250.46
## muertes.permil ~ Pobla80 + PoblaDens + Diabetes + BCG + Camas + 
##     PBI
## 
##                Df Sum of Sq     RSS    AIC
## - PoblaDens     1     12288 1026728 1250.1
## <none>                      1014440 1250.5
## + HipTen.M      1      5691 1008749 1251.7
## + DisMort       1      4271 1010169 1251.9
## + Mujeres       1      2577 1011863 1252.1
## + Medicos       1      2448 1011992 1252.1
## + TempMarzo     1      1618 1012822 1252.2
## + ImmunSaramp   1      1161 1013279 1252.3
## + PoblaData     1       863 1013577 1252.3
## + PoblaMid      1       335 1014105 1252.4
## + Lesion        1        37 1014403 1252.5
## + Tuberculosis  1        27 1014413 1252.5
## + Urbano        1         1 1014439 1252.5
## - Diabetes      1     31008 1045448 1252.6
## - PBI           1     62358 1076798 1256.8
## - Camas         1     69617 1084057 1257.7
## - Pobla80       1    196620 1211060 1273.1
## - BCG           1    340051 1354491 1288.6
## 
## Step:  AIC=1250.13
## muertes.permil ~ Pobla80 + Diabetes + BCG + Camas + PBI
## 
##                Df Sum of Sq     RSS    AIC
## <none>                      1026728 1250.1
## + PoblaDens     1     12288 1014440 1250.5
## + DisMort       1      3874 1022854 1251.6
## + HipTen.M      1      3728 1023000 1251.6
## - Diabetes      1     28119 1054847 1251.9
## + Medicos       1      1169 1025558 1252.0
## + PoblaMid      1       967 1025760 1252.0
## + Lesion        1       899 1025829 1252.0
## + ImmunSaramp   1       868 1025860 1252.0
## + Mujeres       1       745 1025983 1252.0
## + PoblaData     1       585 1026142 1252.0
## + TempMarzo     1       272 1026456 1252.1
## + Tuberculosis  1        37 1026691 1252.1
## + Urbano        1        13 1026715 1252.1
## - PBI           1     51489 1078216 1254.9
## - Camas         1     67763 1094490 1257.0
## - Pobla80       1    205816 1232544 1273.5
## - BCG           1    342848 1369576 1288.2
## 
## Call:
## lm(formula = muertes.permil ~ Pobla80 + Diabetes + BCG + Camas + 
##     PBI, data = df_covid_muertes.permil)
## 
## Coefficients:
## (Intercept)      Pobla80     Diabetes          BCG        Camas          PBI  
##     267.056       30.484       -3.972     -243.886      -13.117        1.057

El mejor modelo resultante del proceso de selección ha sido:

muertes.permil_modelo <- (lm(formula = muertes.permil ~ Pobla80 + Diabetes + BCG + Camas + PBI, data = df_covid_muertes.permil))
summary(muertes.permil_modelo)
## 
## Call:
## lm(formula = muertes.permil ~ Pobla80 + Diabetes + BCG + Camas + 
##     PBI, data = df_covid_muertes.permil)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -233.64  -27.33   -7.42   14.81  435.50 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  267.0564    41.4189   6.448 1.93e-09 ***
## Pobla80       30.4844     5.9039   5.163 8.64e-07 ***
## Diabetes      -3.9723     2.0813  -1.909  0.05848 .  
## BCG         -243.8862    36.5964  -6.664 6.45e-10 ***
## Camas        -13.1168     4.4273  -2.963  0.00361 ** 
## PBI            1.0571     0.4093   2.583  0.01089 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 87.86 on 133 degrees of freedom
## Multiple R-squared:  0.5322, Adjusted R-squared:  0.5146 
## F-statistic: 30.26 on 5 and 133 DF,  p-value: < 2.2e-16

Es recomendable mostrar el intervalo de confianza para cada uno de los coeficientes parciales de regresión:

confint(lm(formula = muertes.permil ~ Pobla80 + Diabetes + BCG + Camas + 
    PBI, data = df_covid_muertes.permil))
##                   2.5 %      97.5 %
## (Intercept)  185.131408  348.981304
## Pobla80       18.806712   42.162176
## Diabetes      -8.089130    0.144497
## BCG         -316.272511 -171.499961
## Camas        -21.873783   -4.359874
## PBI            0.247487    1.866761

Conclusion de obtener los mejores modelos para ambas variables, es mejor utilizar aquel que considera como variable de respuesta las muertes en forma logaritima porque presenta un \(R^2\) superior, un menor \(p-value\) y \(RSE\), por ende sus intervalos de confianza son más cortos también

Comparacion modelo PBI vs modelo final

Considerando como criterios de evaluación las salidas obtenidas de los modelos y su análisis de residuos, el modelo final es mejor porque cumple con los principios “obligatorios” para realizar una regresión lineal y mejores valores en cuanto a salidas. A su vez el modelo final es más sencillo ya que esta compuesto por un menor número de variables y su interpretación es más facil.

Dado que ambos modelos son regresiones podríamos utilizar como métrica el como métrica el Mean Square Error (MSE), pero para esto deberíamos dividir nuestra muestra en test y train y volver a generar los modelos.

Dividimos el dataset en train y test

split <- sample.split(df_covid_total$l10muertes.permil, SplitRatio = 2/3)
training_set <- subset(df_covid_total, split==T)
test_set     <- subset(df_covid_total, split==F)
training_set
##       Pobla80 PoblaMid   PoblaData   PoblaDens  Mujeres  Urbano DisMort Lesion
## 1   0.2771541 54.32490  0.37172386  0.56937760 48.63585  25.495    29.8   19.5
## 2   2.7410163 68.58239  0.02866376  1.04612263 49.06309  60.319    17.0    4.0
## 3   1.2709846 63.48882  0.42228429  0.17730075 49.48427  72.629    14.2    9.5
## 4   0.2723907 50.97470  0.30809762  0.24713052 50.53046  65.514    16.5    9.2
## 5   2.6111754 64.12128  0.44494502  0.16258510 51.23735  91.870    15.8    6.5
## 7   4.0436339 65.15291  0.24992369  0.03249129 50.19962  86.012     9.1    5.9
## 8   5.2369767 66.70049  0.08847037  1.07206927 50.82943  58.297    11.4    5.2
## 10  0.9964088 67.13559  1.61356039 12.39579312 49.38730  36.632    21.6    7.5
## 11  3.8045048 68.28891  0.09485386  0.46728800 53.45605  78.595    23.7    7.0
## 12  5.6948380 64.15583  0.11422068  3.77214927 50.59332  98.001    11.4    6.4
## 13  1.0395255 64.98378  0.00383071  0.16793994 50.19252  45.724    22.1   13.2
## 15  1.2095158 68.22563  0.00754394  0.19777528 47.00264  40.895    23.3   10.5
## 16  1.6248620 61.73450  0.11353142  0.10480146 49.78340  69.425    17.2   13.1
## 17  3.3861895 68.76346  0.03323929  0.64920488 51.01054  48.245    17.8    3.7
## 18  0.4939442 61.66318  0.02254126  0.03977425 51.73076  69.446    20.3    8.3
## 20  0.7583938 72.10039  0.00428962  0.81396964 48.03618  77.629    16.6    7.4
## 22  0.2453268 52.64494  0.19751535  0.72191283 50.09548  29.358    21.7   11.0
## 26  4.3071336 66.89774  0.37058856  0.04075308 50.39153  81.411     9.8    6.1
## 27  0.3528256 52.87991  0.04666377  0.07490412 50.43647  41.364    23.1   10.3
## 28  1.7597254 68.44406  0.49648685  0.44748702 50.92577  80.778    15.8   15.0
## 29  0.3889863 57.45457  0.00832322  4.47244478 49.55870  28.965    22.9   10.9
## 30  0.2877278 55.55478  0.05244363  0.15356846 50.06560  66.916    16.7    9.9
## 31  5.5638202 65.04263  0.04089400  0.73077198 51.85262  56.947    16.7    5.3
## 34  0.6099089 65.89830  0.00958920  0.41368421 47.36679  77.777    19.6   10.4
## 35  1.5873319 64.94005  0.10627165  2.19978576 50.00780  81.074    19.0   12.0
## 37  0.7910340 60.97150  0.98423595  0.98873469 49.46997  42.704    27.7    5.8
## 38  1.8419292 64.58083  0.06420744  3.09881467 53.11400  72.023    14.0   15.4
## 39  0.2886712 60.42552  0.01308974  0.46665740 44.45585  72.143    22.0   10.7
## 40  5.5491132 64.01657  0.01320884  0.30386105 52.85843  68.880    17.0    4.5
## 42  0.5795601 65.03770  0.00883483  0.48357033 49.30050  56.248    30.6    5.4
## 43  5.4057724 62.13403  0.05518050  0.18156856 50.72076  85.382    10.2    5.5
## 47  3.6827447 65.33927  0.03731000  0.65275202 52.29124  58.632    24.9    3.6
## 48  6.6270801 64.91701  0.82927922  2.37370970 50.66037  77.312    12.1    4.0
## 50  7.2679122 64.27348  0.10727668  0.83224732 50.91620  79.058    12.4    2.9
## 51  1.0548473 60.75253  0.17247807  1.60953779 50.75935  51.054    14.9   15.7
## 52  0.3195615 53.22380  0.12414318  0.50522212 51.82111  36.140    22.4    9.2
## 53  1.2722362 65.33518  0.00779004  0.03957348 49.80802  26.606    30.5   12.4
## 55  1.0293956 63.56850  0.09587522  0.85687032 50.04934  57.096    14.0   19.6
## 56  4.2713096 66.43028  0.09768785  1.07906606 52.43243  71.351    23.0    4.4
## 58  2.7008155 68.71630  0.18729160  0.25189446 50.72703  87.564    12.4    7.1
## 59  1.7877871 71.20211 13.92730000  1.48348833 48.67937  59.152    17.0    7.0
## 60  0.9448065 66.76674 13.52617328  4.54938073 48.02354  34.030    23.3   11.3
## 61  0.8561815 67.59164  2.67663435  1.47752190 49.64388  55.325    26.4    6.0
## 62  1.1233084 69.33887  0.81800269  0.50222420 49.43913  74.898    14.8   10.1
## 63  0.4831038 58.28983  0.38433600  0.88530570 49.40868  70.473    21.3   28.4
## 64  3.0188525 64.72778  0.04853506  0.70452983 50.42551  63.170    10.3    4.3
## 66  7.1734357 63.91920  0.60431283  2.05450748 51.37667  70.438     9.5    3.8
## 67  2.0456797 67.45326  0.02934855  2.70993075 50.33983  55.674    14.7    8.7
## 69  0.6023762 61.90802  0.09956011  1.12142498 49.38968  90.979    19.2   10.9
## 70  1.4932044 64.14760  0.18276499  0.06769826 51.51148  57.428    26.8    9.5
## 71  0.2699139 57.87865  0.51393010  0.90299417 50.31602  27.030    13.4    9.6
## 72  0.2327419 75.91064  0.04137309  2.32172222 39.54817 100.000    17.4   12.8
## 74  1.4828557 66.90176  0.06848925  6.69494135 49.70581  88.593    17.9    5.8
## 80  0.4241700 56.34513  0.26262368  0.45139856 50.12397  37.191    22.9   10.6
## 82  1.0741699 69.33310  0.31528585  0.95962821 48.57852  76.036    17.2    8.9
## 85  2.0227961 70.73213  0.01265303  6.23301970 50.57711  40.793    22.6    4.8
## 88  0.6283914 65.50689  0.03170208  0.02040609 50.66954  68.445    30.2   10.6
## 92  0.7791075 67.84431  0.53708395  0.82238615 51.80790  30.579    24.2    8.6
## 93  0.6151794 59.45372  0.02448255  0.02973746 51.55369  50.032    21.3    9.8
## 94  0.7516195 63.85806  0.28087871  1.95939107 54.53534  19.740    21.8    8.8
## 95  4.6976392 64.69565  0.17231017  5.11457910 50.22094  91.490    11.2    5.2
## 96  3.7311137 64.69414  0.04885500  0.18554176 50.83777  86.538    10.1    6.0
## 97  1.1025688 64.55082  0.06465513  0.53727048 50.71271  58.522    14.2   12.7
## 100 0.4596337 75.36071  0.04829483  0.15604145 34.01408  84.539    17.8   17.7
## 101 0.6545620 60.41741  2.12215030  2.75289319 48.53807  36.666    24.7    7.3
## 103 1.2135437 64.12928  0.06956071  0.17508359 49.15161  61.585    17.5   12.0
## 104 1.6809015 66.12100  0.31989256  0.24991606 50.33776  77.907    12.6   10.5
## 105 0.7893793 63.91439  1.06651922  3.57688305 49.74166  46.907    26.8    7.5
## 108 0.1847213 85.08917  0.02781677  2.39593196 24.49529  99.135    15.3   25.9
## 109 4.7084438 66.12674  0.19473936  0.84639847 51.34374  53.998    21.4    3.6
## 110 0.3624706 57.08623  0.12301939  4.98659870 50.86106  17.211    18.2   13.5
## 111 2.4482249 67.86891  0.00110210  2.82589744 49.20878  52.198    23.2    5.6
## 114 4.0191136 65.96453  0.06982084  0.79831740 51.00252  56.092    19.1    3.0
## 115 0.3522140 55.97380  0.07650154  1.05987171 50.12127  42.055    30.5    8.9
## 116 2.1766595 76.25834  0.05638676 79.52998418 47.65813 100.000     9.3    3.7
## 117 3.2223629 68.92462  0.05447011  1.13290578 51.33833  53.726    17.2    6.0
## 118 5.3152643 65.37135  0.02067372  1.02639860 50.24521  54.541    12.7    6.6
## 119 0.7184723 65.60251  0.57779622  0.47630120 50.69415  66.355    26.2    9.1
## 120 3.2120425 72.60812  0.51635256  5.29652104 49.91688  81.459     7.8   10.0
## 121 6.1672057 65.95449  0.46723749  0.93529058 50.89664  80.321     9.9    3.5
## 122 1.6065781 65.32978  0.21670000  3.45558922 51.96682  18.476    17.4    9.7
## 124 5.1493834 66.46583  0.08516543  2.15521378 50.42712  73.797     8.6    6.1
## 125 0.5234433 60.19383  0.09100837  0.65572714 49.58384  27.134    25.3    7.6
## 126 2.5315654 71.01212  0.69428524  1.35897207 51.26870  49.949    14.5   10.2
## 128 1.7197442 67.51420  0.11565204  0.74441323 50.43715  68.945    16.1    6.4
## 130 0.2057545 51.12849  0.42723139  2.13061734 50.77608  23.774    21.9   12.7
## 131 3.9876216 67.75290  0.44622516  0.77029667 53.68775  69.352    24.7    5.0
## 132 0.1307769 84.31149  0.09630959  1.35609110 30.63669  86.522    16.8   16.8
## 134 3.8751036 65.48331  3.27167434  0.35766089 50.52001  82.256    14.6    6.6
## 135 4.3947641 64.57750  0.03449299  0.19708028 51.72154  95.334    16.7    7.5
## 136 0.8369038 66.89480  0.32955400  0.77469205 50.13736  50.478    24.5    6.0
## 139 0.4222739 54.65941  0.14439018  0.37324591 52.35675  32.209    19.3   12.3
##     Tuberculosis Diabetes ImmunSaramp HipTen.M BCG    Medicos      Camas
## 1          189.0      9.2          64     19.8   1 0.24009091  0.4363636
## 2           18.0      9.0          94     39.4   1 1.21237143  2.9375000
## 3           69.0      6.7          80     23.0   1 1.31202500  1.9000000
## 4          355.0      4.5          50     25.2   1 0.17300000  0.8000000
## 5           27.0      5.9          94     32.9   1 3.57165000  4.6000000
## 7            6.6      5.6          95     32.8   1 3.27402222  3.8783333
## 8            7.1      6.6          94     38.8   1 4.66801111  7.7000000
## 10         221.0      9.2          97     17.4   1 0.39047692  0.5750000
## 11          31.0      5.0          97     45.5   1 4.22154000 11.2000000
## 12           9.0      4.6          96     35.0   0 2.79814444  6.5500000
## 13          30.0     17.1          97     25.1   1 0.96785000  1.1600000
## 15         149.0     10.3          97     20.4   1 0.26431111  1.7333333
## 16         108.0      6.8          89     27.2   1 0.71612500  1.1000000
## 17          25.0      9.0          68     43.2   1 1.77503333  3.3666667
## 18         275.0      5.8          97     41.0   1 0.35774444  2.0000000
## 20          68.0     13.3          99     32.0   1 1.38302727  2.7542857
## 22          48.0      7.3          88     12.4   1 0.04237143  0.6500000
## 26           5.6      7.6          90     23.9   0 2.32944444  3.0714286
## 27         540.0      6.0          49     33.2   1 0.04973333  1.1000000
## 28          33.0      7.4          95     28.7   1 1.70129231  1.3166667
## 29          35.0     12.3          90     41.5   1 0.18323333  2.2000000
## 30         375.0      6.0          75     19.3   1 0.12880000  1.6000000
## 31           8.4      5.4          93     38.5   1 2.80697000  5.6354545
## 34         260.0      5.1          86     22.3   1 0.21512500  1.4571429
## 35          45.0      8.6          95     29.3   1 1.37733333  1.4375000
## 37          12.0     17.2          94     23.9   1 1.73048750  1.5100000
## 38          70.0      8.8          81     30.0   1 1.65428000  0.9900000
## 39         201.0      6.0          30     25.2   1 0.40000000  2.0666667
## 40          13.0      4.2          87     42.9   1 3.34070000  5.3600000
## 42          54.0     14.7          94     25.0   1 0.58042000  2.1475000
## 43           4.7      5.6          96     46.6   1 3.14394000  5.9600000
## 47          80.0      5.8          98     41.0   1 4.62520000  3.1222222
## 48           7.3     10.4          97     34.2   1 3.84022000  8.2555556
## 50           4.5      4.7          97     36.4   1 5.71011111  4.6555556
## 51          26.0     10.0          87     26.5   1 0.62945000  0.6250000
## 52         176.0      2.4          48     41.0   1 0.09196667  0.3000000
## 53          83.0     11.6          98     26.3   1 0.56823333  2.1600000
## 55          37.0      7.3          89     25.7   1 0.57160000  0.7285714
## 56           6.4      6.9          99     47.4   1 3.24861667  7.3000000
## 58          18.0      8.6          93     28.2   1 1.03645000  2.1714286
## 59          61.0      9.2          99     32.3   1 1.57320000  3.4066667
## 60         199.0     10.4          90     29.2   1 0.67390833  0.8000000
## 61         316.0      6.3          75     24.7   1 0.24655000  0.9000000
## 62          14.0      9.6          99     25.9   1 0.98516000  1.4400000
## 63          42.0      8.8          83     23.9   1 0.72533333  1.3100000
## 64           7.0      3.2          92     32.2   1 2.81283000  3.9555556
## 66           7.0      5.0          93     36.5   0 3.94927778  3.6875000
## 67           2.9     11.3          89     33.6   1 0.54227143  1.7571429
## 69           5.0     12.7          92     26.3   1 2.24923636  1.7818182
## 70          68.0      6.1          99     33.1   1 3.60587000  7.4111111
## 71         292.0      3.1          89     36.8   1 0.18122857  1.4000000
## 72          23.0     12.2          99     26.5   1 2.16873636  1.9800000
## 74          11.0     11.2          82     29.6   0 2.55344444  3.3800000
## 80         233.0      4.5          62     32.2   1 0.17360000  0.2500000
## 82          92.0     16.7          96     25.2   1 1.24358000  1.8312500
## 85          13.0     22.0          99     48.4   1 1.45775556  3.2333333
## 88         428.0      4.7          99     30.6   1 2.96358889  6.3266667
## 92         338.0      3.9          93     25.5   1 0.53712000  0.7500000
## 93         524.0      4.5          82     41.9   1 0.37305000  3.0000000
## 94         151.0      7.2          91     22.1   1 0.57297500  2.6500000
## 95           5.3      5.4          93     33.8   0 3.40828889  4.6000000
## 96           7.3      6.2          92     30.9   1 2.70726000  2.5500000
## 97          41.0     11.4          99     26.2   1 0.74390000  0.9222222
## 100          5.9     10.1          99     26.2   1 1.95183077  1.8600000
## 101        265.0     19.9          76     21.1   1 0.84727500  0.7200000
## 103         43.0      9.6          93     27.2   1 0.96393333  1.2857143
## 104        123.0      6.6          85     17.8   1 1.18280000  1.4875000
## 105        554.0      7.1          67     23.5   1 1.25186667  0.5833333
## 108         31.0     15.6          99     36.2   1 2.47302500  1.6400000
## 109         68.0      6.9          90     46.7   1 2.40146667  6.4555556
## 110         59.0      5.1          99     30.4   1 0.09168889  1.6000000
## 111          6.3     11.6          99     30.7   1 0.65870000  3.3000000
## 114         17.0      9.0          92     41.6   1 2.42901111  5.6125000
## 115        298.0      2.4          80     39.7   1 0.02050000  0.4000000
## 116         47.0      5.5          95     22.1   1 1.76859091  2.7457143
## 117          5.8      6.5          96     33.7   1 3.11790000  6.3400000
## 118          5.3      5.9          93     37.2   1 2.59476000  4.6555556
## 119        520.0     12.7          70     45.9   1 0.76988750  2.8000000
## 120         66.0      6.9          98     23.5   1 2.05984615 10.6080000
## 121          9.4      6.9          97     37.1   1 4.15920000  3.1888889
## 122         64.0     10.7          99     25.8   1 0.77147000  3.5500000
## 124          6.4      5.7          96     31.7   1 4.00455000  5.1222222
## 125         84.0      6.1          98     25.4   1 1.78443333  5.2666667
## 126        153.0      7.0          96     23.8   1 0.40473333  2.1000000
## 128         35.0      8.5          96     25.2   1 1.16097143  2.0545455
## 130        200.0      2.5          86     32.7   1 0.10457500  0.7500000
## 131         80.0      6.1          91     50.6   1 3.35196667  8.9222222
## 132          1.0     16.3          99     19.2   1 1.70489167  1.6777778
## 134          3.0     10.8          92     31.8   0 2.52747500  3.0333333
## 135         33.0      7.3          97     38.8   1 4.16766000  2.5250000
## 136         70.0      6.5          96     27.7   1 2.50781250  4.5888889
## 139        210.0      1.8          88     32.0   1 0.06561250  2.3500000
##             PBI TempMarzo l10muertes.permil
## 1     1.8351696      7.60        0.85166698
## 2    11.3351950      6.04        1.09735434
## 3    14.1967389     17.91        1.19736588
## 4     6.7205961     22.78        0.05301271
## 5    20.0684923     17.51        1.08768715
## 7    45.7525548     25.37        0.70937665
## 8    48.9687140      1.42        1.86867832
## 10    3.3061083     25.42        0.64062316
## 11   18.1721809     -0.69        1.37219412
## 12   45.2631622      5.23        2.91424609
## 13    8.0937796     24.45        0.79385771
## 15    8.3417246      5.68        0.00000000
## 16    6.5317860     22.07        1.40930364
## 17   11.6971771      4.01        1.66678153
## 18   16.1336867     24.30        0.15945618
## 20   80.8004129     25.92        0.75300190
## 22    1.6709928     30.63        0.56624131
## 26   44.2264907    -18.72        2.26374838
## 27    0.8532664     26.96        0.08432564
## 28   13.2116325     25.14        1.23486238
## 29    2.6420144     25.20        0.53185125
## 30    5.6636489     25.60        0.66491803
## 31   22.9922117      5.83        1.40989932
## 34    2.7442687     25.75        1.29603122
## 35   13.9050879     22.88        1.65899033
## 37   10.8110341     17.83        0.96804820
## 38    7.2348467     25.70        0.84966889
## 39   30.5908474     25.04        1.00721356
## 40   28.8340001     -2.35        1.70728513
## 42    8.8754441     25.00        0.00000000
## 43   42.8552430     -6.09        1.76134842
## 47    9.5834608      0.72        0.62493108
## 48   46.5762068      3.87        2.01040779
## 50   27.2065489      8.19        1.23366924
## 51    7.5159291     22.94        0.69394933
## 52    2.0022876     27.63        0.43000974
## 53    7.1987245     25.65        1.17956888
## 55    4.4336960     23.38        1.32704478
## 56   25.7573736      5.44        1.72513328
## 58   22.2523960     10.87        1.66184336
## 59   13.5313781      0.49        0.63650295
## 60    5.8366566     23.45        0.63846947
## 61   10.5772045     25.79        0.81312576
## 62   18.4501839     11.33        1.97066822
## 63   15.8895142     15.12        0.74455172
## 64   59.3055780      6.00        2.52768884
## 66   37.7630816      6.52        2.73899149
## 67    8.5085469     23.44        0.60923050
## 69    9.2014965     13.08        0.27966158
## 70   24.1030338     -3.96        0.48064752
## 71    2.8839337     26.10        0.31600904
## 72   75.8307296     19.23        1.63646809
## 74   13.2450688     10.39        0.68089875
## 80    1.7044431     23.99        0.03187466
## 82   25.8714034     25.19        0.66721788
## 85   19.3987280     25.48        0.94954834
## 88   11.1277975     -8.80        0.00000000
## 92    5.0214146     22.74        0.04599322
## 93   10.2230967     22.67        0.00000000
## 94    2.4752041     10.02        0.05782208
## 95   49.9843155      4.95        2.53367393
## 96   36.4995043     13.60        0.74060910
## 97    4.8837325     24.95        0.80708405
## 100  42.4790524     23.40        0.94784219
## 101   4.6501098     16.01        0.84119511
## 103  11.4029323     25.84        0.41184731
## 104  12.3515166     19.99        2.09868025
## 105   7.0082665     25.13        0.97663292
## 108 123.2139364     21.78        1.07132447
## 109  21.6182719      3.40        1.80343313
## 110   1.7889788     19.25        0.00000000
## 111  10.9317631     25.90        0.00000000
## 114  14.9080484      4.91        1.54868038
## 115   1.4959394     27.65        0.83772947
## 116  86.0684237     28.62        0.70577580
## 117  29.0915206      2.40        0.78819906
## 118  31.7402860      3.38        1.72227622
## 119  12.8666891     21.10        1.02339821
## 120  34.6370853      3.66        0.79306494
## 121  34.5888453      8.60        2.76446761
## 122  11.1185327     27.03        0.16478915
## 124  61.3146089      0.09        2.28867018
## 125   2.7293337     -3.08        0.78207686
## 126  15.8571484     27.38        0.26030714
## 128  11.2660561     14.21        0.71183934
## 130   1.8103287     23.68        0.00000000
## 131   8.4417522      1.18        1.18842716
## 132  65.5180899     22.61        1.43897112
## 134  55.0581658      0.06        2.48855760
## 135  20.4797528     21.26        0.86794498
## 136   6.8361061      5.38        0.15375896
## 139   2.5606953     22.92        0.10620013

Volvemos a generar nuestros modelos pero usando el training_set

#Modelo PBI
modelo_pbi2 <- lm(l10muertes.permil ~ PoblaDens+Pobla80*PBI+Urbano*PBI+Tuberculosis*PBI+PBI*Camas+TempMarzo+PBI, data = training_set )
summary(modelo_pbi2)
## 
## Call:
## lm(formula = l10muertes.permil ~ PoblaDens + Pobla80 * PBI + 
##     Urbano * PBI + Tuberculosis * PBI + PBI * Camas + TempMarzo + 
##     PBI, data = training_set)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.00715 -0.32674 -0.05597  0.35975  1.09462 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       4.603e-01  3.352e-01   1.373   0.1735  
## PoblaDens        -9.517e-03  7.495e-03  -1.270   0.2079  
## Pobla80           1.072e-01  8.686e-02   1.234   0.2206  
## PBI               1.644e-02  1.904e-02   0.863   0.3906  
## Urbano            8.902e-03  3.769e-03   2.362   0.0206 *
## Tuberculosis     -6.855e-06  7.100e-04  -0.010   0.9923  
## Camas            -8.978e-02  6.541e-02  -1.372   0.1737  
## TempMarzo        -1.214e-02  7.288e-03  -1.666   0.0996 .
## Pobla80:PBI       2.317e-03  2.635e-03   0.879   0.3819  
## PBI:Urbano       -1.344e-04  1.858e-04  -0.723   0.4717  
## PBI:Tuberculosis -5.225e-05  6.448e-05  -0.810   0.4201  
## PBI:Camas         3.849e-04  2.384e-03   0.161   0.8721  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.48 on 80 degrees of freedom
## Multiple R-squared:  0.6237, Adjusted R-squared:  0.572 
## F-statistic: 12.05 on 11 and 80 DF,  p-value: 5.591e-13
# Predicciones de entrenamiento

predicciones_train <- predict(modelo_pbi2, newdata = training_set)

# MSE de entrenamiento

training_mse <- mean((predicciones_train - training_set$l10muertes.permil)^2)
paste("Error (mse) de entrenamiento:", training_mse)
## [1] "Error (mse) de entrenamiento: 0.200319271965709"
# Predicciones de test
predicciones_test <- predict(modelo_pbi2, newdata = test_set)

# MSE de test

test_mse_ols <- mean((predicciones_test - test_set$l10muertes.permil)^2)
paste("Error (mse) de test:", test_mse_ols)
## [1] "Error (mse) de test: 0.252551087377933"
#Modelo final
l10muertes_modelo_2 <- (lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + Tuberculosis + ImmunSaramp + BCG + Camas + PBI +TempMarzo,  data = training_set))
summary(l10muertes_modelo_2)
## 
## Call:
## lm(formula = l10muertes.permil ~ Pobla80 + PoblaDens + Urbano + 
##     Tuberculosis + ImmunSaramp + BCG + Camas + PBI + TempMarzo, 
##     data = training_set)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.89907 -0.30115 -0.01224  0.29783  1.10788 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.5278406  0.5323421   2.870  0.00522 ** 
## Pobla80       0.1961412  0.0395877   4.955 3.83e-06 ***
## PoblaDens    -0.0104219  0.0064053  -1.627  0.10756    
## Urbano        0.0048566  0.0031053   1.564  0.12168    
## Tuberculosis -0.0006070  0.0004863  -1.248  0.21550    
## ImmunSaramp  -0.0061950  0.0047734  -1.298  0.19799    
## BCG          -0.4489113  0.2291737  -1.959  0.05353 .  
## Camas        -0.0654528  0.0340884  -1.920  0.05832 .  
## PBI           0.0077048  0.0031269   2.464  0.01583 *  
## TempMarzo    -0.0095123  0.0069944  -1.360  0.17756    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4713 on 82 degrees of freedom
## Multiple R-squared:  0.6281, Adjusted R-squared:  0.5873 
## F-statistic: 15.39 on 9 and 82 DF,  p-value: 2.295e-14
# Predicciones de entrenamiento

predicciones_train <- predict(l10muertes_modelo_2, newdata = training_set)

# MSE de entrenamiento

training_mse <- mean((predicciones_train - training_set$l10muertes.permil)^2)
paste("Error (mse) de entrenamiento:", training_mse)
## [1] "Error (mse) de entrenamiento: 0.197962139913156"
# Predicciones de test
predicciones_test <- predict(l10muertes_modelo_2, newdata = test_set)

# MSE de test

test_mse_step <- mean((predicciones_test - test_set$l10muertes.permil)^2)
paste("Error (mse) de test:", test_mse_step)
## [1] "Error (mse) de test: 0.237134745321555"

Comparación

df_comparacion <- data.frame(
                    modelo = c("ols", "Stepwise"),
                    mse    = c(test_mse_ols,test_mse_step)
)

ggplot(data = df_comparacion, aes(x = modelo, y = mse)) +
  geom_col(width = 0.5) +
  geom_text(aes(label = round(mse, 2)), vjust = -0.1) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Practicamente los modelos son iguales la diferencia en el MSE es pequeña, es menor el del stepwise y teniendo encuenta el estudio de residuos es preferible el modelo final obtenido.

Recomendaciones

Modelo final

\[\hat{l10muertes.pormil} = 1.58+0.18\hat{Pobla80}-0.0004\hat{Tuber}\] \[-0.007\hat{Urbano}-0.005\hat{ImmunSaramp}\] \[-0.08\hat{Camas}-0.01\hat{TempMarzo}+0.007\hat{PBI}\] \[+0.02\hat{ExpectVida}-0.012\hat{PoblaDens}-0.54\hat{BCG}\]

Teniendo encuenta el modelo, contexto de cuando se recolectaron los datos y objetivo principal de ppder realentecer la curva.

Las recomendaciones que se pueden realizar es que:

La población mayor a 80 años es aquella más vulnerable, por lo que se recomiendan, extremas medidas de cuidado uso de tapaboca y limpieza de manos, reducir las salidas (intentar hacerlo cuando el flujo de personas sea menor y la temperatura en caso de estar en invierno sea alta y en verano moderada).

Incorporar camas y médicos al sistema de salud, por lo que se podría decidir que los residentes o medicos recien recibidos que no han iniciado su recidencia colaboren en otras áreas capacitandolos. Seleccionar médicos que esten entre los 27 y 50 años que capacitandolos puedan colaborar.

Debido al posible agotamiento que puede generar en los especialistas incluiria acompañamiento psicologico e intentaría ver la manera de que cuando el sistema no este en su pico, tengan más descanso. Dado que hay estudios que demuestran que la salud es una convinatorio de los físico y mental

En aquellos países cuya temperatura no sea muy fría recomendaria que hay más libertad de circulación principalmente para no desarrollar otras patologías provocadas por el encierro y poca sociabilización.

En caso de contar con una vacuna, se deberán vacunar primero a los médicos que esten expuestos y población de 80 años.

Aquellos países con PBI bajo que no puedan contar con capital para la compra de materiales o generar más médicos deberán optar por medidas más estrictas y buscar apoyo económico, porque su economía puede verse resentida.